{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:58:26Z","timestamp":1785862706610,"version":"3.56.0"},"reference-count":281,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009594","name":"University of P\u00e9cs","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009594","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.<\/jats:p>","DOI":"10.1007\/s10462-024-11100-x","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T08:48:14Z","timestamp":1737103694000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":408,"title":["Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9252-2903","authenticated-orcid":false,"given":"Abhishek","family":"Upadhyay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narendra Singh","family":"Chandel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subir Kumar","family":"Chakraborty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balaji M.","family":"Nandede","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohit","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Subeesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konga","family":"Upendar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Salem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Elbeltagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"11100_CR1","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11119-019-09703-4","volume":"21","author":"J Abdulridha","year":"2019","unstructured":"Abdulridha J, Ampatzidis Y, Kakarla SC, Roberts P (2019) Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precis Agric 21:955\u2013978","journal-title":"Precis Agric"},{"issue":"11","key":"11100_CR2","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.3390\/rs9111110","volume":"9","author":"T Adao","year":"2017","unstructured":"Adao T, Hru\u0161ka J, P\u00e1dua L, Bessa J, Peres E, Morais R, Sousa JJ (2017) Hyperspectral imaging: a review on UAV-Based sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens 9(11):1110. https:\/\/doi.org\/10.3390\/rs9111110","journal-title":"Remote Sens"},{"key":"11100_CR3","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-981-15-3125-5_17","volume-title":"Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies","author":"VV Adit","year":"2020","unstructured":"Adit VV, Rubesh CV, Bharathi SS, Santhiya G, Anuradha R (2020) A comparison of Deep Learning algorithms for Plant Disease classification. Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol 643. Springer, Singapore, pp 153\u2013161"},{"key":"11100_CR4","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","volume":"167","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S (2020) ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Comput Sci 167:293\u2013301","journal-title":"Procedia Comput Sci"},{"key":"11100_CR5","doi-asserted-by":"publisher","unstructured":"Aggarwal A, Mittal M, Battineni G (2021) Generative adversarial network: an overview of theory and applications. Int J Inf Manag Data Insights 1(1). https:\/\/doi.org\/10.1016\/j.jjimei.2020.100004","DOI":"10.1016\/j.jjimei.2020.100004"},{"key":"11100_CR6","doi-asserted-by":"crossref","first-page":"121339","DOI":"10.1016\/j.saa.2022.121339","volume":"278","author":"DK Agustika","year":"2022","unstructured":"Agustika DK, Mercuriani I, Purnomo CW, Hartono S, Triyana K, Iliescu DD, Leeson MS (2022) Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected Chilli plants. Spectrochim Acta Mol Biomol Spectrosc 278:121339","journal-title":"Spectrochim Acta Mol Biomol Spectrosc"},{"key":"11100_CR7","doi-asserted-by":"publisher","first-page":"8812019","DOI":"10.1155\/2020\/8812019","volume":"2020","author":"JI Ahmad","year":"2020","unstructured":"Ahmad JI, Hamid M, Yousaf S, Shah ST, Ahmad MO (2020) Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity 2020:8812019. https:\/\/doi.org\/10.1155\/2020\/8812019","journal-title":"Complexity"},{"issue":"1","key":"11100_CR8","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/diagnostics13010159","volume":"13","author":"A Ait Nasser","year":"2022","unstructured":"Ait Nasser A, Akhloufi MA (2022) A review of recent advances in deep learning models for chest disease detection using radiography. Diagnostics 13(1):159. https:\/\/doi.org\/10.3390\/diagnostics13010159","journal-title":"Diagnostics"},{"issue":"1","key":"11100_CR9","doi-asserted-by":"crossref","first-page":"12","DOI":"10.2991\/ijcis.d.200108.001","volume":"13","author":"JSH Al-bayati","year":"2020","unstructured":"Al-bayati JSH, \u00dcst\u00fcnda\u011f BB (2020) Evolutionary feature optimization for plant leaf disease detection by deep neural networks. Int J Comput Intell Syst 13(1):12\u201323","journal-title":"Int J Comput Intell Syst"},{"key":"11100_CR13","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1017\/S2040470017000802","volume":"8","author":"H Al-Saddik","year":"2017","unstructured":"Al-Saddik H, Simon JC, Brousse O, Cointault F (2017) Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence dor\u00e9e. Adv Anim Biosci 8:150\u2013155","journal-title":"Adv Anim Biosci"},{"key":"11100_CR10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3390\/rs11010023","volume":"11","author":"J Albetis","year":"2018","unstructured":"Albetis J, Jacquin A, Goulard M, Poilv\u00e9 H, Rousseau J, Clenet H, Dedieu G, Duthoit S (2018) On the potentiality of UAV multispectral imagery to detect Flavescence dor\u00e9e and grapevine trunk diseases. Remote Sens 11:23","journal-title":"Remote Sens"},{"key":"11100_CR11","doi-asserted-by":"publisher","first-page":"1355941","DOI":"10.3389\/fpls.2024.1355941","volume":"15","author":"EA Aldakheel","year":"2024","unstructured":"Aldakheel EA, Zakariah M, Alabdalall AH (2024) Detection and identification of plant leaf diseases using YOLOv4. Front Plant Sci 15:1355941. https:\/\/doi.org\/10.3389\/fpls.2024.1355941","journal-title":"Front Plant Sci"},{"key":"11100_CR12","doi-asserted-by":"crossref","unstructured":"Ale L, Sheta A, Li L, Wang Y, Zhang N (2019) Deep learning-based plant disease detection for smart agriculture. In: Proc IEEE Globecom Workshops, Waikoloa, HI, USA, pp 1\u20136","DOI":"10.1109\/GCWkshps45667.2019.9024439"},{"key":"11100_CR14","doi-asserted-by":"publisher","first-page":"3998193","DOI":"10.1155\/2022\/3998193","volume":"2022","author":"H Alshammari","year":"2022","unstructured":"Alshammari H, Gasmi K, Ltaifa IB, Krichen M, Ammar LB, Mahmood A (2022) Olive disease classification based on Vision Transformer and CNN models. Comput Intell Neurosci 2022:3998193. https:\/\/doi.org\/10.1155\/2022\/3998193","journal-title":"Comput Intell Neurosci"},{"key":"11100_CR15","volume-title":"A deep learning-based approach for banana leaf diseases classification","author":"J Amara","year":"2017","unstructured":"Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: Datenbanksysteme f\u00fcr Business, Technologie und Web, Stuttgart"},{"key":"11100_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/ICRTIT.2016.7569531","author":"R Anand","year":"2016","unstructured":"Anand R, Veni S, Aravinth J (2016) An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. Proc 2016 Int Conf Recent Trends Inf Technol ICRTIT. https:\/\/doi.org\/10.1109\/ICRTIT.2016.7569531","journal-title":"Proc 2016 Int Conf Recent Trends Inf Technol ICRTIT"},{"key":"11100_CR17","first-page":"012088","volume-title":"IOP conf ser: Earth Environ Sci","author":"N Anasta","year":"2021","unstructured":"Anasta N, Setyawan F, Fitriawan H (2021) Disease detection in banana trees using an image processing-based thermal camera. IOP conf ser: Earth Environ Sci. IOP Publishing, Bristol, UK, p 012088"},{"key":"11100_CR19","unstructured":"Anonymous (2018b) India outranks US, China with world\u2019s highest net cropland area. Archived from the original on 18 November 2018. Retrieved January 17, 2024"},{"key":"11100_CR18","unstructured":"Anonymous (2018a) ILO modelled estimates database. ILOSTAT. International Labour Organization. Accessed February 07, 2024. https:\/\/www.ilo.org\/industries-and-sectors\/agriculture-plantations-other-rural-sectors#events"},{"key":"11100_CR20","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.3390\/rs6064723","volume":"6","author":"D Ashourloo","year":"2014","unstructured":"Ashourloo D, Mobasheri MR, Huete A (2014) Developing two spectral disease indices for detection of wheat leaf rust (Puccinia Triticina). Remote Sens 6:4723\u20134740","journal-title":"Remote Sens"},{"issue":"12","key":"11100_CR21","first-page":"10","volume":"2","author":"B Ashqar","year":"2019","unstructured":"Ashqar B, Abu-Naser S (2019) Image-based tomato leaves disease detection using deep learning. Int J Eng Res 2(12):10\u201316","journal-title":"Int J Eng Res"},{"key":"11100_CR22","doi-asserted-by":"crossref","first-page":"160014","DOI":"10.1063\/1.5091341","volume":"2075","author":"S Atanassova","year":"2019","unstructured":"Atanassova S, Nikolov P, Valchev N, Masheva S, Yorgov D (2019) Early detection of powdery mildew (Podosphaera Xanthii) on cucumber leaves based on visible and near-infrared spectroscopy. AIP Conf Proc 2075:160014","journal-title":"AIP Conf Proc"},{"key":"11100_CR23","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"U Atila","year":"2021","unstructured":"Atila U, U\u00e7ar M, Akyol K, U\u00e7ar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inf 61:101182","journal-title":"Ecol Inf"},{"key":"11100_CR24","unstructured":"Atole RR, Park D (2018) A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies. Int J Adv Comput Sci Appl"},{"key":"11100_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.11.016","author":"M Azadbakht","year":"2019","unstructured":"Azadbakht M, Ashourloo D, Aghighi H, Radiom S, Alimohammadi A (2019) Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques. Comput Electron Agric. https:\/\/doi.org\/10.1016\/j.compag.2018.11.016","journal-title":"Comput Electron Agric"},{"key":"11100_CR26","doi-asserted-by":"crossref","unstructured":"Baranowski P, Jedryczka M, Mazurek W, Babula-Skowronska D, Siedliska A, Kaczmarek J (2015) Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE 10","DOI":"10.1371\/journal.pone.0122913"},{"key":"11100_CR27","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.biosystemseng.2018.05.013","volume":"172","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018a) Factors influencing the use of deep learning for plant disease recognition. Biosyst Eng 172:84\u201391","journal-title":"Biosyst Eng"},{"key":"11100_CR28","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","volume":"153","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018b) Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric 153:46\u201353","journal-title":"Comput Electron Agric"},{"key":"11100_CR29","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.03.012","volume":"147","author":"JGA Barbedo","year":"2016","unstructured":"Barbedo JGA, Koenigkan LV, Santos TT (2016) Identifying multiple plant diseases using digital image processing. Biosyst Eng 147:104\u2013116","journal-title":"Biosyst Eng"},{"issue":"3","key":"11100_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs13030516","volume":"13","author":"Y Bazi","year":"2021","unstructured":"Bazi Y, Bashmal L, Al Rahhal MM, Dayil RA, Ajlan NA (2021) Vision transformers for remote sensing image classification. Remote Sens 13(3):1\u201320. https:\/\/doi.org\/10.3390\/rs13030516","journal-title":"Remote Sens"},{"issue":"8","key":"11100_CR31","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11100_CR32","doi-asserted-by":"crossref","first-page":"4878","DOI":"10.1109\/TGRS.2017.2655365","volume":"55","author":"EF Berra","year":"2017","unstructured":"Berra EF, Gaulton R, Barr S (2017) Commercial off-the-shelf digital cameras on unmanned aerial vehicles for multitemporal monitoring of vegetation reflectance and NDVI. IEEE Trans Geosci Remote Sens 55:4878\u20134886","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11100_CR33","doi-asserted-by":"publisher","unstructured":"Bhandari M, Shahi TB, Neupane A, Walsh KB (2023) BotanicX-AI: identification of tomato leaf diseases using an explanation-driven deep-learning model. J Imaging 9(2). https:\/\/doi.org\/10.3390\/jimaging9020053","DOI":"10.3390\/jimaging9020053"},{"key":"11100_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2019.2956080","volume":"PP","author":"S Bi","year":"2019","unstructured":"Bi S, Zhang Y, Dong M, Min H (2019) An embedded inference framework for convolutional neural network applications. IEEE Access PP:1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2019.2956080","journal-title":"IEEE Access"},{"key":"11100_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.34133\/2019\/9209727","volume":"2019","author":"A Bierman","year":"2019","unstructured":"Bierman A, LaPlumm T, Cadle-Davidson L, Gadoury D, Martinez D, Sapkota S et al (2019) A high-throughput phenotyping system using machine vision to quantify severity of grapevine powdery mildew. Plant Phenom 2019:1\u201313. https:\/\/doi.org\/10.34133\/2019\/9209727","journal-title":"Plant Phenom"},{"key":"11100_CR36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42483-019-0043-5","volume":"2","author":"CH Bock","year":"2020","unstructured":"Bock CH, Barbedo JG, Del Ponte EM, Bohnenkamp D, Mahlein AK (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy. Phytopathol Res 2:1\u201330","journal-title":"Phytopathol Res"},{"issue":"4","key":"11100_CR38","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","volume":"31","author":"M Brahimi","year":"2017","unstructured":"Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299\u2013315","journal-title":"Appl Artif Intell"},{"key":"11100_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-90403-0_6","volume-title":"Human and Machine Learning","author":"M Brahimi","year":"2018","unstructured":"Brahimi M, Arsenovic M, Laraba S, Sladojevic S, Boukhalfa K, Moussaoui A (2018) Deep learning for plant diseases: detection and saliency map visualization. In: Zhou J, Chen F (eds) Human and Machine Learning. Human\u2013Computer Interaction Series. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-90403-0_6"},{"key":"11100_CR39","unstructured":"Breton M, Eng P (2019) Overview of two performance metrics for object detection algorithms evaluation. Defence Research and Development Canada Reference Document"},{"issue":"1","key":"11100_CR40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.chemolab.2005.05.004","volume":"80","author":"CD Brown","year":"2006","unstructured":"Brown CD, Davis HT (2006) Receiver operating characteristics curves and related decision measures: a tutorial. Chemometr Intell Lab Syst 80(1):24\u201338","journal-title":"Chemometr Intell Lab Syst"},{"key":"11100_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2013.07.031","author":"R Calderon","year":"2013","unstructured":"Calderon R, Navas-Cort\u00e9s JA, Lucena C, Zarco-Tejada PJ (2013) High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature, and narrow-band spectral indices. Remote Sens Environ. https:\/\/doi.org\/10.1016\/j.rse.2013.07.031","journal-title":"Remote Sens Environ"},{"key":"11100_CR41","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11119-014-9360-y","volume":"15","author":"R Calderon","year":"2014","unstructured":"Calderon R, Montes-Borrego M, Landa BB, Navas-Cort\u00e9s JA, Zarco-Tejada PJ (2014) Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precis Agric 15:639\u2013661","journal-title":"Precis Agric"},{"key":"11100_CR43","doi-asserted-by":"crossref","unstructured":"Cap HQ, Suwa K, Fujita E, Kagiwada S, Uga H, Iyatomi H (2018) A deep learning approach for on-site plant leaf detection. In Proc. IEEE 14th Int Colloq Signal Process & Its Appl (CSPA), Batu Feringghi, pp. 118\u2013122","DOI":"10.1109\/CSPA.2018.8368697"},{"key":"11100_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12862-017-1014-z","volume":"17","author":"J Carranza-Rojas","year":"2017","unstructured":"Carranza-Rojas J, Goeau H, Bonnet P, Mata-Montero E, Joly A (2017) Going deeper in the automated identification of herbarium specimens. BMC Evol Biol 17:1\u201314","journal-title":"BMC Evol Biol"},{"issue":"23","key":"11100_CR45","doi-asserted-by":"crossref","first-page":"20539","DOI":"10.1007\/s00521-022-07744-x","volume":"34","author":"SK Chakraborty","year":"2022","unstructured":"Chakraborty SK, Chandel NS, Jat D, Tiwari MK, Rajwade YA, Subeesh A (2022) Deep learning approaches and interventions for futuristic engineering in agriculture. Neural Comput Appl 34(23):20539\u201320573","journal-title":"Neural Comput Appl"},{"issue":"23","key":"11100_CR47","doi-asserted-by":"crossref","first-page":"3344","DOI":"10.3390\/plants11233344","volume":"11","author":"NS Chandel","year":"2022","unstructured":"Chandel NS, Rajwade YA, Dubey K, Chandel AK, Subeesh A, Tiwari MK (2022) Water stress identification of winter wheat crop with state-of-the-art AI techniques and high-resolution thermal-RGB imagery. Plants 11(23):3344","journal-title":"Plants"},{"key":"11100_CR46","doi-asserted-by":"crossref","first-page":"107863","DOI":"10.1016\/j.engappai.2024.107863","volume":"131","author":"NS Chandel","year":"2024","unstructured":"Chandel NS, Chakraborty SK, Chandel AK, Dubey K, Subeesh A, Jat D, Rajwade YA (2024) State-of-the-art AI-enabled mobile device for real-time water stress detection of field crops. Eng Appl Artif Intell 131:107863","journal-title":"Eng Appl Artif Intell"},{"key":"11100_CR51","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","volume":"7","author":"Y Chen","year":"2014","unstructured":"Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2094\u20132107","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"11100_CR50","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54:6232\u20136251","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11100_CR48","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.compag.2018.12.036","volume":"156","author":"T Chen","year":"2019","unstructured":"Chen T, Zhang J, Chen Y, Wan S, Zhang L (2019a) Detection of peanut leaf spots disease using canopy hyperspectral reflectance. Comput Electron Agric 156:677\u2013683","journal-title":"Comput Electron Agric"},{"key":"11100_CR49","unstructured":"Chen Y, Li L, Yu L, Kholy AE, Ahmed F, Gan Z, Cheng Y, Liu J (2019b) UNITER: UNiversal Image-TExt Representation Learning. ArXiv. \/abs\/1909.11740"},{"key":"11100_CR52","doi-asserted-by":"publisher","unstructured":"Cheng S, Cheng H, Yang R, Zhou J, Li Z, Shi B, Lee M, Ma Q (2023) A high performance wheat disease detection based on position information. Plants 12(5). https:\/\/doi.org\/10.3390\/plants12051191","DOI":"10.3390\/plants12051191"},{"key":"11100_CR53","doi-asserted-by":"crossref","first-page":"6549","DOI":"10.3390\/rs6076549","volume":"6","author":"C Cilia","year":"2014","unstructured":"Cilia C, Panigada C, Rossini M, Meroni M, Busetto L, Amaducci S, Boschetti M, Picchi V, Colombo R (2014) Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens 6:6549\u20136565","journal-title":"Remote Sens"},{"key":"11100_CR54","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1093\/jxb\/ert029","volume":"64","author":"JM Costa","year":"2013","unstructured":"Costa JM, Grant OM, Chaves MM (2013) Thermography to explore plant-environment interactions. J Exp Bot 64:3937\u20133949","journal-title":"J Exp Bot"},{"key":"11100_CR55","doi-asserted-by":"crossref","unstructured":"Cruz A, Luvisi A, De Bellis L, Ampatzidis Y (2017) Vision-based plant disease detection system using transfer and deep learning. In Proc ASABE Annu Int Meet, Spokane, WA, USA, 2017","DOI":"10.13031\/aim.201700241"},{"key":"11100_CR56","doi-asserted-by":"publisher","first-page":"107411","DOI":"10.1016\/j.compag.2022.107411","volume":"2022","author":"R Cui","year":"2022","unstructured":"Cui R, Li J, Wang Y, Fang S, Yu K, Zhao Y (2022) Hyperspectral imaging coupled with dual-channel convolutional neural network for early detection of apple valsa canker. Comput Electron Agric 2022:107411. https:\/\/doi.org\/10.1016\/j.compag.2022.107411","journal-title":"Comput Electron Agric"},{"key":"11100_CR57","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1007\/s13593-015-0319-9","volume":"35","author":"P Damos","year":"2015","unstructured":"Damos P (2015) Modular structure of web-based decision support systems for integrated pest management: a review. Agron Sustain Dev 35:1347\u20131372. https:\/\/doi.org\/10.1007\/s13593-015-0319-9","journal-title":"Agron Sustain Dev"},{"key":"11100_CR58","doi-asserted-by":"crossref","unstructured":"Dandawate Y, Kokare R (2015) An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. In Proc IEEE Int Conf Adv Comput Commun Inf (ICACCI), Kochi, India","DOI":"10.1109\/ICACCI.2015.7275707"},{"key":"11100_CR59","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.3390\/rs10081216","volume":"10","author":"J Dash","year":"2018","unstructured":"Dash J, Pearse G, Watt M (2018) UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sens 10:1216","journal-title":"Remote Sens"},{"key":"11100_CR60","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1094\/PHYTO-11-16-0417-R","volume":"107","author":"C DeChant","year":"2017","unstructured":"DeChant C, Wiesner-Hanks T, Chen S, Stewart EL, Yosinski J, Gore MA et al (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107:1426\u20131432. https:\/\/doi.org\/10.1094\/PHYTO-11-16-0417-R","journal-title":"Phytopathology"},{"key":"11100_CR61","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3390\/agronomy4030349","volume":"4","author":"D Deery","year":"2014","unstructured":"Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4:349\u2013379","journal-title":"Agronomy"},{"key":"11100_CR62","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1051\/agro:2008039","volume":"29","author":"A Dell\u2019 Aquila","year":"2009","unstructured":"Dell\u2019 Aquila A (2009) Digital imaging information technology applied to seed germination testing: a review. Agron Sustain Dev 29:213\u2013221. https:\/\/doi.org\/10.1051\/agro:2008039","journal-title":"Agron Sustain Dev"},{"issue":"1","key":"11100_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-023-00863-9","volume":"11","author":"WB Demilie","year":"2024","unstructured":"Demilie WB (2024) Plant disease detection and classification techniques: a comparative study of the performances. J Big Data 11(1):1\u201343. https:\/\/doi.org\/10.1186\/s40537-023-00863-9","journal-title":"J Big Data"},{"key":"11100_CR64","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. In Proc IEEE Conf Comput Vis Pattern Recognit, Miami, FL, USA, 20\u201325 June 2009; pp. 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11100_CR65","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","volume":"146","author":"L Deng","year":"2018","unstructured":"Deng L, Mao Z, Li X, Hu Z, Duan F, Yan Y (2018) UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras. J Photogramm Remote Sens 146:124\u2013136","journal-title":"J Photogramm Remote Sens"},{"key":"11100_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.05.262","author":"AK Dey","year":"2016","unstructured":"Dey AK, Sharma M, Meshram MR (2016) Image processing-based leaf rot disease detection of betel vine (Piper betle L). Procedia Comput Sci. https:\/\/doi.org\/10.1016\/j.procs.2016.05.262","journal-title":"Procedia Comput Sci"},{"key":"11100_CR67","doi-asserted-by":"publisher","unstructured":"Dey P, Mahmud T, Nahar SR, Hossain MS, Andersson K (2024) Plant disease detection in precision agriculture: Deep learning approaches. In 2nd Int Conf Intell Data Commun Technol Internet Things (IDCIoT), Bengaluru, India, 2024, pp. 661\u2013667. https:\/\/doi.org\/10.1109\/IDCIoT59759.2024.10467525","DOI":"10.1109\/IDCIoT59759.2024.10467525"},{"issue":"1","key":"11100_CR68","doi-asserted-by":"crossref","first-page":"26","DOI":"10.14445\/22312803\/IJCTT-V61P105","volume":"61","author":"A Dhakal","year":"2018","unstructured":"Dhakal A, Shakya S (2018) Image-based plant disease detection with deep learning. Int J Comput Trends Technol 61(1):26\u201329","journal-title":"Int J Comput Trends Technol"},{"issue":"15","key":"11100_CR69","doi-asserted-by":"publisher","first-page":"19951","DOI":"10.1007\/s11042-017-5445-8","volume":"77","author":"G Dhingra","year":"2018","unstructured":"Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools Appl 77(15):19951\u201320000. https:\/\/doi.org\/10.1007\/s11042-017-5445-8","journal-title":"Multimedia Tools Appl"},{"key":"11100_CR70","first-page":"262","volume":"55","author":"SF Di Gennaro","year":"2016","unstructured":"Di Gennaro SF, Battiston E, Di Marco S, Facini O, Matese A, Nocentini M, Palliotti A, Mugnai L (2016) UAV-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathol Mediterr 55:262\u2013275","journal-title":"Phytopathol Mediterr"},{"key":"11100_CR71","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16\u00d716 words: transformers for image recognition at scale. ArXiv. http:\/\/arxiv.org\/abs\/2010.11929"},{"key":"11100_CR72","doi-asserted-by":"crossref","unstructured":"Durmu\u015f H, G\u00fcnes E\u00d6, K\u0131rc\u0131 M (2017) Disease detection on the leaves of tomato plants using deep learning. In Proc 6th Int Conf Agro-Geoinformatics, Fairfax, VA, USA, 7\u201310 August 2017; pp. 1\u20135","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"key":"11100_CR73","doi-asserted-by":"crossref","first-page":"105162","DOI":"10.1016\/j.compag.2019.105162","volume":"169","author":"JGM Esgario","year":"2020","unstructured":"Esgario JGM, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169:105162","journal-title":"Comput Electron Agric"},{"key":"11100_CR74","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The Pascal Visual object classes (VOC) challenge. Int J Comput Vis 88:303\u2013338","journal-title":"Int J Comput Vis"},{"issue":"8","key":"11100_CR75","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874","journal-title":"Pattern Recognit Lett"},{"key":"11100_CR76","doi-asserted-by":"publisher","first-page":"106252","DOI":"10.1016\/j.compag.2021.106252","volume":"187","author":"A Fazari","year":"2021","unstructured":"Fazari A, Pellicer-Valero OJ, G\u00f3mez-Sanch\u00eds J, Bernardi B, Cubero S, Benalia S, Zimbalatti G, Blasco J (2021) Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS\/NIR hyperspectral images. Comput Electron Agric 187:106252. https:\/\/doi.org\/10.1016\/j.compag.2021.106252","journal-title":"Comput Electron Agric"},{"key":"11100_CR77","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318","journal-title":"Comput Electron Agric"},{"key":"11100_CR78","unstructured":"Fergus R (2012) Deep learning methods for vision. CVPR 2012 Tutorial"},{"issue":"9","key":"11100_CR79","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim S, Park D (2017) A robust deep learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022","journal-title":"Sensors"},{"key":"11100_CR80","doi-asserted-by":"publisher","unstructured":"Gadepally KC, Dhal SB, Bhandari M, Landivar J, Kalafatis S, Nowka K (2023) A Deep Transfer Learning-based approach for forecasting spatio-temporal features to maximize yield in cotton crops. In 2023 57th Annual Conference on Information Sciences and Systems (CISS), IEEE, pp 1\u20134. https:\/\/doi.org\/10.1109\/CISS56502.2023.10089748","DOI":"10.1109\/CISS56502.2023.10089748"},{"key":"11100_CR81","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.agwat.2015.01.020","volume":"153","author":"J Gago","year":"2015","unstructured":"Gago J, Douthe C, Coopman R, Gallego P, Ribas-Carbo M, Flexas J, Escalona J, Medrano H (2015) UAVs challenge to assess water stress for sustainable agriculture. Agric Water Manag 153:9\u201319","journal-title":"Agric Water Manag"},{"key":"11100_CR82","first-page":"673","volume":"58","author":"R Gallo","year":"2017","unstructured":"Gallo R, Ristorto G, Daglio G, Berta G, Lazzari M, Mazzetto F (2017) New solutions for the automatic early detection of diseases in vineyards through ground sensing approaches integrating LiDAR and optical sensors. Chem Eng Trans 58:673\u2013678","journal-title":"Chem Eng Trans"},{"key":"11100_CR83","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","volume":"76","author":"G Geetharamani","year":"2019","unstructured":"Geetharamani G, Pandian JA (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323\u2013338","journal-title":"Comput Electr Eng"},{"issue":"4","key":"11100_CR84","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/agriculture6010004","volume":"6","author":"J Geipel","year":"2016","unstructured":"Geipel J, Link J, Wirwahn J, Claupein W (2016) A programmable aerial multispectral camera system for in-season crop biomass and nitrogen content estimation. Agriculture 6(4):4","journal-title":"Agriculture"},{"key":"11100_CR85","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","volume":"8","author":"CM Gevaert","year":"2015","unstructured":"Gevaert CM, Suomalainen J, Tang J, Kooistra L (2015) Generation of spectral\u2013temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J Sel Top Appl Earth Obs Remote Sens 8:3140\u20133146","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"11100_CR86","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 11\u201318 December 2015","DOI":"10.1109\/ICCV.2015.169"},{"key":"11100_CR87","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 20\u201323 June 2014","DOI":"10.1109\/CVPR.2014.81"},{"key":"11100_CR88","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2020.08.025","volume":"169","author":"MS Gomez","year":"2020","unstructured":"Gomez MS, Vergara A, Montenegro F, Alonso Ruiz H, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB et al (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: a case study in DR Congo and Republic of Benin. J Photogramm Remote Sens 169:110\u2013124","journal-title":"J Photogramm Remote Sens"},{"key":"11100_CR89","doi-asserted-by":"crossref","unstructured":"Goncharov P, Ososkov G, Nechaevskiy A, Nestsiarenia I (2019) Disease detection on the plant leaves by deep learning. In Selected Papers from the XX International Conference on Neuro-informatics, Advances in Neural Computation, Machine Learning, and Cognitive Research II, pp 151\u2013159, Moscow, Russia","DOI":"10.1007\/978-3-030-01328-8_16"},{"key":"11100_CR90","doi-asserted-by":"publisher","unstructured":"Gong X, Zhang S (2023) A high-precision detection method of apple leaf diseases using improved faster R-CNN. Agric (Switzerland) 13(2). https:\/\/doi.org\/10.3390\/agriculture13020240","DOI":"10.3390\/agriculture13020240"},{"key":"11100_CR91","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In Advances in Neural Information Processing Systems, pp 2672\u20132680"},{"key":"11100_CR92","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s10658-015-0640-9","volume":"142","author":"E Granum","year":"2015","unstructured":"Granum E, P\u00e9rez-Bueno ML, Calder\u00f3n CE, Ramos C, de Vicente A, Cazorla FM, Bar\u00f3n M (2015) Metabolic responses of avocado plants to stress induced by Rosellinia Necatrix analysed by fluorescence and thermal imaging. Eur J Plant Pathol 142:625\u2013632","journal-title":"Eur J Plant Pathol"},{"key":"11100_CR93","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418\u2013424","journal-title":"Comput Electron Agric"},{"key":"11100_CR94","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/agronomy9020054","volume":"9","author":"E Gruner","year":"2019","unstructured":"Gruner E, Astor T, Wachendorf M (2019) Biomass prediction of heterogeneous temperate grasslands using an SfM approach based on UAV imaging. Agronomy 9:54","journal-title":"Agronomy"},{"issue":"4","key":"11100_CR95","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.3390\/agriengineering5040110","volume":"5","author":"SV Gudkov","year":"2023","unstructured":"Gudkov SV, Matveeva TA, Sarimov RM, Simakin AV, Stepanova EV, Moskovskiy MN, Dorokhov AS, Izmailov AY (2023) Optical methods for the detection of plant pathogens and diseases (review). AgriEngineering 5(4):1789\u20131812. https:\/\/doi.org\/10.3390\/agriengineering5040110","journal-title":"AgriEngineering"},{"key":"11100_CR96","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang J, Yin C et al (2020) Plant disease identification based on deep learning algorithm in smart farming. Discrete Dyn Nat Soc 2020:2479172","DOI":"10.1155\/2020\/2479172"},{"key":"11100_CR97","doi-asserted-by":"crossref","first-page":"106597","DOI":"10.1016\/j.asoc.2020.106597","volume":"96","author":"S Hernandez","year":"2020","unstructured":"Hernandez S, L\u00f3pez JL (2020) Uncertainty quantification for plant disease detection using bayesian deep learning. Appl Soft Comput 96:106597","journal-title":"Appl Soft Comput"},{"issue":"5786","key":"11100_CR98","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov R (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"11100_CR99","doi-asserted-by":"crossref","unstructured":"Hlaing CS, Zaw SMM (2018) Tomato plant diseases classification using statistical texture feature and colour feature. In Proc IEEE\/ACIS 17th Int Conf Comput Inf Sci, Singapore","DOI":"10.1109\/ICIS.2018.8466483"},{"key":"11100_CR101","doi-asserted-by":"publisher","unstructured":"Hu W, Huang Y, Wei L, Zhang F, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens 2015:258619. https:\/\/doi.org\/10.1155\/2015\/258619","DOI":"10.1155\/2015\/258619"},{"key":"11100_CR100","doi-asserted-by":"publisher","first-page":"7673","DOI":"10.1039\/C9RA00805E","volume":"9","author":"F Hu","year":"2019","unstructured":"Hu F, Zhou M, Yan P, Li D, Lai W, Bian K, Dai R (2019) Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network. RSC Adv 9:7673\u20137679. https:\/\/doi.org\/10.1039\/C9RA00805E","journal-title":"RSC Adv"},{"key":"11100_CR102","unstructured":"Huang Z, Zeng Z, Liu B, Fu D, Fu J (2020) Pixel-BERT: aligning image pixels with text by deep multi-modal transformers. arXiv. https:\/\/arxiv.org\/abs\/2004.00849"},{"key":"11100_CR103","unstructured":"Hughes DP, Salathe M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowd sourcing. https:\/\/arxiv.org\/abs\/1511.08060"},{"key":"11100_CR104","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/22797254.2018.1432293","volume":"51","author":"F Iqbal","year":"2018","unstructured":"Iqbal F, Lucieer A, Barry K (2018) Simplified radiometric calibration for UAS-mounted multispectral sensor. Eur J Remote Sens 51:301\u2013313","journal-title":"Eur J Remote Sens"},{"key":"11100_CR105","doi-asserted-by":"publisher","unstructured":"Jajja AI, Abbas A, Khattak HA, Niedba\u0142a G, Khalid A, Rauf HT, Kujawa S (2022) Compact Convolutional Transformer (CCT)-based approach for whitefly attack detection in cotton crops. Agric (Switz) 12(10). https:\/\/doi.org\/10.3390\/agriculture12101529","DOI":"10.3390\/agriculture12101529"},{"key":"11100_CR106","doi-asserted-by":"publisher","first-page":"59069","DOI":"10.1109\/ACCESS.2019.2914929","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang P, Chen Y, Liu B, He D, Liang C (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069\u201359080. https:\/\/doi.org\/10.1109\/ACCESS.2019.2914929","journal-title":"IEEE Access"},{"key":"11100_CR108","doi-asserted-by":"publisher","first-page":"395","DOI":"10.3390\/rs10030395","volume":"10","author":"X Jin","year":"2018","unstructured":"Jin X, Jie L, Wang S, Qi H, Li S (2018) Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens 10:395. https:\/\/doi.org\/10.3390\/rs10030395","journal-title":"Remote Sens"},{"key":"11100_CR107","doi-asserted-by":"publisher","first-page":"107055","DOI":"10.1016\/j.compag.2022.107055","volume":"198","author":"H Jin","year":"2022","unstructured":"Jin H, Li Y, Qi J, Feng J, Tian D, Mu W (2022) GrapeGAN: unsupervised image enhancement for improved grape leaf disease recognition. Comput Electron Agric 198:107055. https:\/\/doi.org\/10.1016\/j.compag.2022.107055","journal-title":"Comput Electron Agric"},{"key":"11100_CR109","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.1094\/PDIS-12-18-2267-RE","volume":"103","author":"M Kalischuk","year":"2019","unstructured":"Kalischuk M, Paret ML, Freeman JH, Raj D, Da Silva S, Eubanks S, Wiggins DJ, Lollar M, Marois JJ, Mellinger HC et al (2019) An improved crop scouting technique incorporating unmanned aerial vehicle-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Dis 103:1642\u20131650. https:\/\/doi.org\/10.1094\/PDIS-12-18-2267-RE","journal-title":"Plant Dis"},{"key":"11100_CR110","doi-asserted-by":"publisher","first-page":"102213","DOI":"10.1016\/j.ecoinf.2023.102213","volume":"77","author":"R Karthik","year":"2023","unstructured":"Karthik R, Joshua Alfred J, Joel Kennedy J (2023) Inception-based global context attention network for the classification of coffee leaf diseases. Ecol Inf 77:102213. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102213","journal-title":"Ecol Inf"},{"issue":"17","key":"11100_CR111","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.17485\/IJST\/v17i17.536","volume":"17","author":"K Kaur","year":"2024","unstructured":"Kaur K, Bansal K (2024) Enhancing plant disease detection using advanced deep learning models. Indian J Sci Technol 17(17):1755\u20131766. https:\/\/doi.org\/10.17485\/IJST\/v17i17.536","journal-title":"Indian J Sci Technol"},{"key":"11100_CR112","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.compag.2018.10.015","volume":"155","author":"M Kerkech","year":"2018","unstructured":"Kerkech M, Hafiane A, Canals R (2018) Deep learning approach with colourimetric spaces and vegetation indices for vine disease detection in UAV images. Comput Electron Agric 155:237\u2013243. https:\/\/doi.org\/10.1016\/j.compag.2018.10.015","journal-title":"Comput Electron Agric"},{"key":"11100_CR113","doi-asserted-by":"publisher","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","volume":"174","author":"M Kerkech","year":"2020","unstructured":"Kerkech M, Hafiane A, Canals R (2020) Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Comput Electron Agric 174:105446. https:\/\/doi.org\/10.1016\/j.compag.2020.105446","journal-title":"Comput Electron Agric"},{"issue":"1","key":"11100_CR114","doi-asserted-by":"publisher","first-page":"012002","DOI":"10.1088\/1755-1315\/951\/1\/012002","volume":"951","author":"A Khakimov","year":"2022","unstructured":"Khakimov A, Salakhutdinov I, Omolikov A, Utaganov S (2022) Traditional and current-prospective methods of agricultural plant diseases detection: a review. IOP Conf Ser Earth Environ Sci 951(1):012002. https:\/\/doi.org\/10.1088\/1755-1315\/951\/1\/012002","journal-title":"IOP Conf Ser Earth Environ Sci"},{"issue":"35\u201336","key":"11100_CR115","doi-asserted-by":"publisher","first-page":"25763","DOI":"10.1007\/s11042-020-08841-z","volume":"79","author":"MA Khan","year":"2020","unstructured":"Khan MA, Akram T, Sharif M, Saba T (2020) Fruit diseases classification: exploiting a hierarchical framework for deep features fusion and selection. Multimed Tools Appl 79(35\u201336):25763\u201325783. https:\/\/doi.org\/10.1007\/s11042-020-08841-z","journal-title":"Multimed Tools Appl"},{"key":"11100_CR116","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1080\/01431161.2015.1112015","volume":"37","author":"LR Khot","year":"2015","unstructured":"Khot LR, Sankaran S, Carter AH, Johnson DA, Cummings TF (2015) UAS imaging-based decision tools for arid winter wheat and irrigated potato production management. Int J Remote Sens 37:125\u2013137. https:\/\/doi.org\/10.1080\/01431161.2015.1112015","journal-title":"Int J Remote Sens"},{"key":"11100_CR117","doi-asserted-by":"publisher","unstructured":"Krithika N, Selvarani AG (2017) An individual grape leaf disease identification using leaf skeletons and KNN classification. In: Proceedings of the International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS 2017), Coimbatore, India, 2017-January, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICIIECS.2017.8275951","DOI":"10.1109\/ICIIECS.2017.8275951"},{"key":"11100_CR119","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1109\/LSP.2020.2984815","volume":"27","author":"S Kumar","year":"2020","unstructured":"Kumar S, Singh SK (2020) Occluded thermal face recognition using bag of CNN (BoCNN). IEEE Signal Process Lett 27:975\u2013979. https:\/\/doi.org\/10.1109\/LSP.2020.2984815","journal-title":"IEEE Signal Process Lett"},{"issue":"5","key":"11100_CR118","doi-asserted-by":"publisher","first-page":"63","DOI":"10.9734\/jeai\/2023\/v45i52132","volume":"45","author":"M Kumar","year":"2023","unstructured":"Kumar M, Chandel NS, Singh D, Rajput LS (2023) Soybean disease detection and segmentation based on Mask-RCNN algorithm. J Exp Agric Int 45(5):63\u201372. https:\/\/doi.org\/10.9734\/jeai\/2023\/v45i52132","journal-title":"J Exp Agric Int"},{"key":"11100_CR120","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14:778\u2013782. https:\/\/doi.org\/10.1109\/LGRS.2017.2681128","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11100_CR121","unstructured":"Lawaniya H (2020) Computer vision. IET Computer Vision. https:\/\/github.com\/himanshu6670\/iot-object-detection-"},{"key":"11100_CR123","doi-asserted-by":"crossref","unstructured":"Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-Plant: Plant identification with convolutional neural networks. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 27\u201330 September 2015; pp. 452\u2013456","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"11100_CR122","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1\u201313","journal-title":"Pattern Recogn"},{"key":"11100_CR124","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s00299-016-2083-y","volume":"36","author":"R Lei","year":"2017","unstructured":"Lei R, Jiang H, Hu F, Yan J, Zhu S (2017) Chlorophyll fluorescence lifetime imaging provides new insight into the chlorosis induced by plant virus infection. Plant Cell Rep 36:327\u2013341","journal-title":"Plant Cell Rep"},{"issue":"04","key":"11100_CR126","first-page":"504","volume":"16","author":"J Li","year":"2019","unstructured":"Li J, Mi Y, Li G, Ju Z (2019a) CNN-based facial expression recognition from annotated RGB-D images for human\u2013robot interaction. Int J Humanoid Rob 16(04):504\u2013505","journal-title":"Int J Humanoid Rob"},{"key":"11100_CR127","unstructured":"Li LH, Yatskar M, Yin D, Hsieh CJ, Chang KW (2019b) VisualBERT: A simple and performant baseline for vision and language. https:\/\/arxiv.org\/pdf\/1909.11740.pdf"},{"issue":"17","key":"11100_CR130","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20174938","volume":"20","author":"M Li","year":"2020","unstructured":"Li M, Zhang Z, Lei L, Wang X, Guo X (2020a) Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: comparison of faster R-CNN, YOLO v3 and SSD. Sens (Switzerland) 20(17):1\u201314. https:\/\/doi.org\/10.3390\/s20174938","journal-title":"Sens (Switzerland)"},{"key":"11100_CR132","doi-asserted-by":"crossref","unstructured":"Li X, Yin X, Li C, Zhang P, Hu X, Zhang L, Wang L, Hu H, Dong L, Wei F, Choi Y, Gao J (2020b) Oscar: object-semantics aligned pre-training for vision-language tasks. ArXiv. \/abs\/2004.06165","DOI":"10.1007\/978-3-030-58577-8_8"},{"key":"11100_CR129","doi-asserted-by":"publisher","unstructured":"Li M, Zhou G, Chen A, Yi J, Lu C, He M, Hu Y (2022) FWDGAN-based data augmentation for tomato leaf disease identification. Comput Electron Agric 194. https:\/\/doi.org\/10.1016\/j.compag.2022.106779","DOI":"10.1016\/j.compag.2022.106779"},{"key":"11100_CR125","doi-asserted-by":"crossref","unstructured":"Li J, Xu M, Xiang L, Chen D, Zhuang W, Yin X, Li Z (2023a) Large language models and foundation models in smart agriculture: Basics, opportunities, and challenges. ArXiv. \/abs\/2308.06668","DOI":"10.1016\/j.compag.2024.109032"},{"key":"11100_CR128","doi-asserted-by":"publisher","unstructured":"Li M, Cheng S, Cui J, Li C, Li Z, Zhou C, Lv C (2023b) High-performance plant pest and disease detection based on model ensemble with inception module and cluster algorithm. Plants 12(1). https:\/\/doi.org\/10.3390\/plants12010200","DOI":"10.3390\/plants12010200"},{"issue":"6","key":"11100_CR131","doi-asserted-by":"publisher","first-page":"101401","DOI":"10.1016\/j.jksuci.2022.09.013","volume":"35","author":"X Li","year":"2023","unstructured":"Li X, Li X, Zhang S, Zhang G, Zhang M, Shang H (2023c) SLViT: shuffle-convolution-based lightweight vision transformer for effective diagnosis of sugarcane leaf diseases. J King Saud Univ - Comput Inform Sci 35(6):101401. https:\/\/doi.org\/10.1016\/j.jksuci.2022.09.013","journal-title":"J King Saud Univ - Comput Inform Sci"},{"key":"11100_CR133","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.biosystemseng.2017.11.019","volume":"166","author":"P-S Liang","year":"2018","unstructured":"Liang P-S, Haff RP, Hua S-ST, Munyaneza JE, Mustafa T, Sarreal SBL (2018) Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy. Biosyst Eng 166:161\u2013169","journal-title":"Biosyst Eng"},{"key":"11100_CR135","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft COCO: Common objects in context. In: Lecture Notes in Computer Science, Proceedings of the European Conference on Computer Vision\u2014ECCV 2014, Zurich, Switzerland, 6\u201312 September; Springer: Cham, Switzerland, 2014; Volume 8693, pp. 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"11100_CR134","doi-asserted-by":"crossref","first-page":"155","DOI":"10.3389\/fpls.2019.00155","volume":"10","author":"K Lin","year":"2019","unstructured":"Lin K, Gong L, Huang Y, Liu C, Pan J (2019) Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front Plant Sci 10:155","journal-title":"Front Plant Sci"},{"key":"11100_CR138","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: Single shot multibox detector. In: Proceedings of the European Conference on Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands, 8\u201316 October 2016","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"11100_CR139","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u201326","journal-title":"Neurocomputing"},{"key":"11100_CR136","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/sym10010011","volume":"10","author":"B Liu","year":"2018","unstructured":"Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10:11. https:\/\/doi.org\/10.3390\/sym10010011","journal-title":"Symmetry"},{"issue":"18","key":"11100_CR137","doi-asserted-by":"crossref","first-page":"4640","DOI":"10.3390\/rs15184640","volume":"15","author":"C Liu","year":"2023","unstructured":"Liu C, Cao Y, Wu E, Yang R, Xu H, Qiao Y (2023a) A discriminative model for early detection of anthracnose in strawberry plants based on hyperspectral imaging technology. Remote Sens 15(18):4640","journal-title":"Remote Sens"},{"key":"11100_CR140","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu J, Cheng W, Chen Z, Zhou J, Cheng H, Lv C (2023b) A high-precision plant disease detection method based on a dynamic pruning gate friendly to low-computing platforms. Plants 12(11). https:\/\/doi.org\/10.3390\/plants12112073","DOI":"10.3390\/plants12112073"},{"issue":"2","key":"11100_CR141","first-page":"41","volume":"11","author":"M Loey","year":"2020","unstructured":"Loey M, ElSawy A, Afify M (2020) Deep learning in plant diseases detection for agricultural crops. Int J Service Sci Manage Eng Technol 11(2):41\u201358","journal-title":"Int J Service Sci Manage Eng Technol"},{"key":"11100_CR142","doi-asserted-by":"publisher","DOI":"10.3390\/rs8040276","author":"M Lopez-Lopez","year":"2016","unstructured":"Lopez-Lopez M, Calderon R, Gonzalez-Dugo V, Zarco-Tejada PJ, Fereres E (2016) Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sens. https:\/\/doi.org\/10.3390\/rs8040276","journal-title":"Remote Sens"},{"key":"11100_CR143","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","volume":"13","author":"A Lowe","year":"2017","unstructured":"Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13:80","journal-title":"Plant Methods"},{"key":"11100_CR144","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.3389\/fpls.2017.01681","volume":"8","author":"R Ludovisi","year":"2017","unstructured":"Ludovisi R, Tauro F, Salvati R, Khoury S, Mugnozza Scarascia G, Harfouche A (2017) UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought. Front Plant Sci 8:1681","journal-title":"Front Plant Sci"},{"key":"11100_CR145","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s13593-022-00792-6","volume":"42","author":"J MacPherson","year":"2022","unstructured":"MacPherson J, Voglhuber-Slavinsky A, Olbrisch M et al (2022) Future agricultural systems and the role of digitalization for achieving sustainability goals: a review. Agron Sustain Dev 42:70. https:\/\/doi.org\/10.1007\/s13593-022-00792-6","journal-title":"Agron Sustain Dev"},{"key":"11100_CR146","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tplants.2018.11.007","volume":"24","author":"WH Maes","year":"2019","unstructured":"Maes WH, Steppe K (2019) Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci 24:152\u2013164","journal-title":"Trends Plant Sci"},{"key":"11100_CR147","unstructured":"Mahajan U, Bundel BR (2016) Drones for Normalized Difference Vegetation Index (NDVI) to estimate crop health for precision agriculture: A cheaper alternative for spatial satellite sensors. In: International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal Sciences, Biodiversity, Ecological Sciences, and Climate Change. Krishi Sanskriti Publications, New Delhi, India"},{"key":"11100_CR148","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","volume":"100","author":"AK Mahlein","year":"2016","unstructured":"Mahlein AK (2016) Plant disease detection by imaging sensors: parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241\u2013251","journal-title":"Plant Dis"},{"key":"11100_CR150","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10658-011-9878-z","volume":"133","author":"AK Mahlein","year":"2012","unstructured":"Mahlein AK, Oerke EC, Steiner U, Dehne HW (2012) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197\u2013209","journal-title":"Eur J Plant Pathol"},{"key":"11100_CR149","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.3390\/s19102281","volume":"19","author":"AK Mahlein","year":"2019","unstructured":"Mahlein AK, Alisaac E, Al Masri A, Behmann J, Dehne HW, Oerke EC (2019) Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale. Sensors 19:2281","journal-title":"Sensors"},{"issue":"2","key":"11100_CR151","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1145\/3542698","volume":"20","author":"B Mahmud","year":"2023","unstructured":"Mahmud B, Hong G, Fong B (2023) A study of human\u2013AI symbiosis for creative work: recent developments and future directions in deep learning. ACM Trans Multimedia Comput Commun Appl 20(2):47. https:\/\/doi.org\/10.1145\/3542698","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"11100_CR152","doi-asserted-by":"publisher","first-page":"28","DOI":"10.28991\/ESJ-2024-08-01-03","volume":"8","author":"BU Mahmud","year":"2024","unstructured":"Mahmud BU, Al Mamun A, Hossen M, Hong GY, Jahan B (2024) Light-weight deep learning model for accelerating the classification of mango-leaf disease. Emerg Sci J 8:28\u201342. https:\/\/doi.org\/10.28991\/ESJ-2024-08-01-03","journal-title":"Emerg Sci J"},{"key":"11100_CR153","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1134\/S1054660X06050215","volume":"16","author":"LG Marcassa","year":"2006","unstructured":"Marcassa LG, Gasparoto M, Belasque J, Lins E, Dias Nunes F, Bagnato VS (2006) Fluorescence spectroscopy applied to orange trees. Laser Phys 16:884\u2013888","journal-title":"Laser Phys"},{"key":"11100_CR154","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s13593-022-00796-2","volume":"42","author":"T Martin","year":"2022","unstructured":"Martin T, Gasselin P, Hostiou N et al (2022) Robots and transformations of work in farms: a systematic review of the literature and a research agenda. Agron Sustain Dev 42:66. https:\/\/doi.org\/10.1007\/s13593-022-00796-2","journal-title":"Agron Sustain Dev"},{"key":"11100_CR155","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","volume":"35","author":"F Martinelli","year":"2015","unstructured":"Martinelli F, Scalenghe R, Davino S et al (2015) Advanced methods of plant disease detection: a review. Agron Sustain Dev 35:1\u201325. https:\/\/doi.org\/10.1007\/s13593-014-0246-1","journal-title":"Agron Sustain Dev"},{"key":"11100_CR156","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196072","author":"V Martinez-Martinez","year":"2018","unstructured":"Martinez-Martinez V, Gomez-Gil J, Machado ML, Pinto FAC (2018) Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0196072","journal-title":"PLoS ONE"},{"key":"11100_CR157","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-981-15-3383-9_54","volume":"1141","author":"A Mathew","year":"2021","unstructured":"Mathew A, Amudha P, Sivakumari S (2021) Deep learning techniques: an overview. Adv Intell Syst Comput 1141:599\u2013608. https:\/\/doi.org\/10.1007\/978-981-15-3383-9_54","journal-title":"Adv Intell Syst Comput"},{"key":"11100_CR158","doi-asserted-by":"crossref","first-page":"917","DOI":"10.3390\/rs10060917","volume":"10","author":"C Mattupalli","year":"2018","unstructured":"Mattupalli C, Moffet C, Shah K, Young C (2018) Supervised classification of RGB aerial imagery to evaluate the impact of a root rot disease. Remote Sens 10:917","journal-title":"Remote Sens"},{"key":"11100_CR159","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.3390\/app12073391","volume":"12","author":"TA Matveyeva","year":"2022","unstructured":"Matveyeva TA, Sarimov RM, Simakin AV et al (2022) Using fluorescence spectroscopy to detect rot in fruit and vegetable crops. Appl Sci 12:3391","journal-title":"Appl Sci"},{"key":"11100_CR160","doi-asserted-by":"publisher","first-page":"5521","DOI":"10.3390\/app13095521","volume":"13","author":"J Maur\u00edcio","year":"2022","unstructured":"Maur\u00edcio J, Domingues I, Bernardino J (2022) Comparing vision transformers and convolutional neural networks for image classification: a literature review. Appl Sci 13:5521. https:\/\/doi.org\/10.3390\/app13095521","journal-title":"Appl Sci"},{"key":"11100_CR161","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115\u2013133","journal-title":"Bull Math Biophys"},{"key":"11100_CR162","doi-asserted-by":"crossref","first-page":"7977","DOI":"10.1007\/s00500-019-04083-3","volume":"24","author":"P Melnyk","year":"2019","unstructured":"Melnyk P, You Z, Li K (2019) A high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Comput 24:7977\u20137987","journal-title":"Soft Comput"},{"key":"11100_CR163","doi-asserted-by":"crossref","unstructured":"Meunkaewjinda A, Kumsawat P, Attakitmongcol K, Srikaew A (2008) Grape leaf disease detection from color imagery using hybrid intelligent system. In: Proc IEEE 5th Int Conf Electrical Engineering\/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). pp 513\u2013516, Krabi","DOI":"10.1109\/ECTICON.2008.4600483"},{"key":"11100_CR164","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"key":"11100_CR165","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rti.2005.03.003","volume":"11","author":"D Moshou","year":"2005","unstructured":"Moshou D, Bravo C, Oberti R, West J, Bodria L, McCartney A, Ramon H (2005) Plant disease detection based on data fusion of hyperspectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging 11:75\u201383","journal-title":"Real-Time Imaging"},{"key":"11100_CR166","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","volume":"15","author":"K Nagasubramanian","year":"2019","unstructured":"Nagasubramanian K, Jones S, Singh AK, Sarkar S, Singh A, Ganapathysubramanian B (2019) Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15:98","journal-title":"Plant Methods"},{"key":"11100_CR167","first-page":"963","volume":"41","author":"S Nebiker","year":"2016","unstructured":"Nebiker S, Lack N, Ab\u00e4cherli M, L\u00e4derach S (2016) Light-weight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 41:963\u2013970","journal-title":"ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci"},{"issue":"1","key":"11100_CR168","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.inpa.2020.04.004","volume":"8","author":"LC Ngugi","year":"2021","unstructured":"Ngugi LC, Abelwahab M, Abo-Zahhad M (2021) Recent advances in image processing techniques for automated leaf pest and disease recognition \u2013 A review. Inform Process Agric 8(1):27\u201351. https:\/\/doi.org\/10.1016\/j.inpa.2020.04.004","journal-title":"Inform Process Agric"},{"key":"11100_CR169","doi-asserted-by":"publisher","first-page":"742","DOI":"10.3390\/s21030742","volume":"21","author":"C Nguyen","year":"2020","unstructured":"Nguyen C, Sagan V, Maimaitiyiming M, Maimaitijiang M, Bhadra S, Kwasniewski MT (2020) Early detection of plant viral disease using hyperspectral imaging and deep learning. Sensors 21:742. https:\/\/doi.org\/10.3390\/s21030742","journal-title":"Sensors"},{"key":"11100_CR170","doi-asserted-by":"crossref","first-page":"102810","DOI":"10.1016\/j.pce.2019.102810","volume":"115","author":"L Nhamo","year":"2020","unstructured":"Nhamo L, Ebrahim GY, Mabhaudhi T, Mpandeli S, Magombeyi M, Chitakira M, Magidi J, Sibanda M (2020) An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Phys Chem Earth A\/B\/C 115:102810","journal-title":"Phys Chem Earth A\/B\/C"},{"key":"11100_CR171","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.agrformet.2013.09.007","volume":"184","author":"W Nijland","year":"2014","unstructured":"Nijland W, De Jong R, De Jong SM, Wulder MA, Bater CW, Coops NC (2014) Monitoring plant condition and phenology using infrared-sensitive consumer-grade digital cameras. Agric Meteorol 184:98\u2013106","journal-title":"Agric Meteorol"},{"key":"11100_CR172","unstructured":"O\u2019Shea K, Nash R (2015) An introduction to convolutional neural networks. https:\/\/arxiv.org\/abs\/1511.08458"},{"key":"11100_CR173","doi-asserted-by":"publisher","DOI":"10.1093\/jxb\/erw318","author":"EC Oerke","year":"2016","unstructured":"Oerke EC, Herzog K, Toepfer R (2016) Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara Viticola. J Exp Bot. https:\/\/doi.org\/10.1093\/jxb\/erw318","journal-title":"J Exp Bot"},{"key":"11100_CR174","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1017\/S2040470017001376","volume":"8","author":"D Oppenheim","year":"2017","unstructured":"Oppenheim D, Shani G (2017) Potato disease classification using convolutional neural networks. Adv Anim Biosci 8:244\u2013249","journal-title":"Adv Anim Biosci"},{"key":"11100_CR176","first-page":"621","volume":"5","author":"MM Ozguven","year":"2018","unstructured":"Ozguven MM (2018) Determination of sugar beet leaf spot disease level (Cercospora Beticola Sacc.) With image processing technique by using drone. Curr Investig Agric Curr Res 5:621\u2013631","journal-title":"Curr Investig Agric Curr Res"},{"key":"11100_CR175","doi-asserted-by":"crossref","first-page":"122537","DOI":"10.1016\/j.physa.2019.122537","volume":"535","author":"MM Ozguven","year":"2019","unstructured":"Ozguven MM, Adem K (2019) Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Phys a 535:122537","journal-title":"Phys a"},{"key":"11100_CR177","doi-asserted-by":"publisher","unstructured":"Padilla R, Netto SL, da Silva EAB (2020) A survey on performance metrics for object-detection algorithms. In: International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 237\u2013242. https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"11100_CR178","doi-asserted-by":"crossref","unstructured":"Padol PB, Sawant SD (2016) Fusion classification technique used to detect downy and powdery mildew grape leaf diseases. In: Proceedings of the International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2017:298\u2013301","DOI":"10.1109\/ICGTSPICC.2016.7955315"},{"key":"11100_CR179","unstructured":"Parmar N, Vaswani A, Uszkoreit J, Kaiser L, Shazeer N, Ku A, Tran D (2018) Image transformer. In: Proceedings of the International Conference on Machine Learning (ICML)"},{"key":"11100_CR180","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-55","volume":"14","author":"S Paulus","year":"2013","unstructured":"Paulus S, Dupuis J, Mahlein AK, Kuhlmann H (2013) Surface feature-based classification of plant organs from 3D laser-scanned point clouds for plant phenotyping. BMC Bioinformatics 14:1\u201312. https:\/\/doi.org\/10.1186\/1471-2105-14-55","journal-title":"BMC Bioinformatics"},{"key":"11100_CR181","volume-title":"Agro-biological substantiation of the technology of growing vegetable products with the use of biological means of protection. Monograph","author":"NE Pavlovskaya","year":"2018","unstructured":"Pavlovskaya NE, Gagarina IN (2018) In: Borodin DB, Gneusheva IA, Gorkova IV, Solokhina IYu, Kostromicheva EV, Lushnikov AV, Yakovleva IV, Ageyeva NY (eds) Agro-biological substantiation of the technology of growing vegetable products with the use of biological means of protection. Monograph. Publishing House of the Federal State Budgetary Educational Institution of Higher Education OSAU, Orel"},{"key":"11100_CR182","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.compag.2016.04.015","volume":"125","author":"RDL Pires","year":"2016","unstructured":"Pires RDL, Gon\u00e7alves DN, Oru\u00ea JPM et al (2016) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48\u201355. https:\/\/doi.org\/10.1016\/j.compag.2016.04.015","journal-title":"Comput Electron Agric"},{"key":"11100_CR183","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpls.2019.00116","volume":"10","author":"G Polder","year":"2019","unstructured":"Polder G, Blok PM, de Villiers HAC, van der Wolf JM, Kamp J (2019) Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:1\u201312. https:\/\/doi.org\/10.3389\/fpls.2019.00116","journal-title":"Front Plant Sci"},{"issue":"1","key":"11100_CR184","first-page":"37","volume":"2","author":"DM Powers","year":"2011","unstructured":"Powers DM (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Res 2(1):37\u201363","journal-title":"J Mach Learn Res"},{"key":"11100_CR185","first-page":"3311","volume":"22","author":"K Prakash","year":"2017","unstructured":"Prakash K, Saravanamoorthi P, Sathishkumar R, Parimala M (2017) A study of image processing in agriculture. Int J Adv Netw Appl 22:3311\u20133315","journal-title":"Int J Adv Netw Appl"},{"key":"11100_CR186","first-page":"415","volume":"40","author":"C Proctor","year":"2015","unstructured":"Proctor C, He Y (2015) Workflow for building a hyperspectral UAV: challenges and opportunities. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci 40:415\u2013419","journal-title":"ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci"},{"issue":"1\u20132","key":"11100_CR187","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compag.2006.02.001","volume":"52","author":"R Pydipati","year":"2006","unstructured":"Pydipati R, Burks TF, Lee WS (2006) Identification of citrus disease using colour texture features and discriminant analysis. Comput Electron Agric 52(1\u20132):49\u201359. https:\/\/doi.org\/10.1016\/j.compag.2006.02.001","journal-title":"Comput Electron Agric"},{"key":"11100_CR188","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1117\/12.2623039","volume":"12114","author":"Q Qian","year":"2022","unstructured":"Qian Q, Yu K, Yadav PK, Dhal S, Kalafatis S, Thomasson JA, Hardin IVRG (2022) Cotton crop disease detection on remotely collected aerial images with deep learning. In: Autonomous Air and Ground Sensing systems for Agricultural optimization and phenotyping VII. SPIE 12114:23\u201331. https:\/\/doi.org\/10.1117\/12.2623039","journal-title":"SPIE"},{"key":"11100_CR189","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3390\/rs13010162","volume":"13","author":"J Qin","year":"2021","unstructured":"Qin J, Wang B, Wu Y, Lu Q, Zhu H (2021) Identifying pine wood nematode disease using UAV images and deep learning algorithms. Remote Sens 13:162. https:\/\/doi.org\/10.3390\/rs13010162","journal-title":"Remote Sens"},{"issue":"22","key":"11100_CR190","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.3390\/rs11222658","volume":"11","author":"R Qiu","year":"2019","unstructured":"Qiu R, Yang C, Moghimi A, Zhang M, Steffenson BJ, Hirsch CD (2019) Detection of fusarium head blight in wheat using a deep neural network and colour imaging. Remote Sens 11(22):2658. https:\/\/doi.org\/10.3390\/rs11222658","journal-title":"Remote Sens"},{"key":"11100_CR191","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning transferable visual models from natural language supervision. arXiv preprint. https:\/\/arxiv.org\/pdf\/2103.00020.pdf"},{"issue":"6","key":"11100_CR192","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/bdcc8060052","volume":"8","author":"P Rado\u010daj","year":"2024","unstructured":"Rado\u010daj P, Rado\u010daj D, Martinovi\u0107 G (2024) Image-based leaf disease recognition using transfer deep learning with a novel versatile optimization module. Big Data Cogn Comput 8(6):52. https:\/\/doi.org\/10.3390\/bdcc8060052","journal-title":"Big Data Cogn Comput"},{"key":"11100_CR193","unstructured":"Ramachandran P, Parmar N, Vaswani A, Bello I, Levskaya A, Shlens J (2019) Stand-alone self-attention in vision models. In: Proceedings of NeurIPS"},{"key":"11100_CR194","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.3389\/fpls.2017.01852","volume":"8","author":"A Ramcharan","year":"2017","unstructured":"Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852. https:\/\/doi.org\/10.3389\/fpls.2017.01852","journal-title":"Front Plant Sci"},{"key":"11100_CR195","doi-asserted-by":"publisher","first-page":"272","DOI":"10.3389\/fpls.2019.00272","volume":"10","author":"A Ramcharan","year":"2019","unstructured":"Ramcharan A, McCloskey P, Baranowski K (2019) A mobile-based deep learning model for cassava disease diagnosis. Front Plant Sci 10:272. https:\/\/doi.org\/10.3389\/fpls.2019.00272","journal-title":"Front Plant Sci"},{"key":"11100_CR196","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.196","volume":"133","author":"A Rangarajan","year":"2018","unstructured":"Rangarajan A, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput Sci 133:1040\u20131047. https:\/\/doi.org\/10.1016\/j.procs.2018.07.196","journal-title":"Procedia Comput Sci"},{"key":"11100_CR197","doi-asserted-by":"publisher","unstructured":"Raza S-, Clarkson JP, Rajpoot NM (2015) Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10. https:\/\/doi.org\/10.1371\/journal.pone.0123262","DOI":"10.1371\/journal.pone.0123262"},{"key":"11100_CR198","doi-asserted-by":"publisher","DOI":"10.1007\/s10772-021-09882-6","author":"SRG Reddy","year":"2021","unstructured":"Reddy SRG, Varma GPS, Davuluri RL (2021) Optimized convolutional neural network model for plant species identification from leaf images using computer vision. Int J Speech Technol. https:\/\/doi.org\/10.1007\/s10772-021-09882-6","journal-title":"Int J Speech Technol"},{"key":"11100_CR199","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: Better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21\u201326 July 2017","DOI":"10.1109\/CVPR.2017.690"},{"key":"11100_CR200","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. arXiv preprint. https:\/\/arxiv.org\/abs\/1804.02767"},{"key":"11100_CR201","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016","DOI":"10.1109\/CVPR.2016.91"},{"key":"11100_CR202","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11100_CR203","doi-asserted-by":"publisher","first-page":"044515","DOI":"10.1117\/1.JARS.14.044515","volume":"14","author":"CA Rivera-Romero","year":"2020","unstructured":"Rivera-Romero CA, Palacios-Hern\u00e1ndez ER, Trejo-Dur\u00e1n M, Rodr\u00edguez-Li\u00f1\u00e1n MdC, Olivera-Reyna R, Morales-Salda\u00f1a JA (2020) Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves. J Appl Remote Sens 14:044515. https:\/\/doi.org\/10.1117\/1.JARS.14.044515","journal-title":"J Appl Remote Sens"},{"key":"11100_CR204","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s11119-017-9448-6","volume":"19","author":"L Roth","year":"2017","unstructured":"Roth L, Streit B (2017) Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach. Precis Agric 19:93\u2013114. https:\/\/doi.org\/10.1007\/s11119-017-9448-6","journal-title":"Precis Agric"},{"key":"11100_CR205","doi-asserted-by":"publisher","unstructured":"Rothe PR, Kshirsagar RV (2014) Automated extraction of digital images features of three kinds of cotton leaf diseases. In: Proceedings of the International Conference on Electronics, Communication and Computer Engineering (ICECCE), 2014:67\u201371. https:\/\/doi.org\/10.1109\/ICECCE.2014.7086637","DOI":"10.1109\/ICECCE.2014.7086637"},{"issue":"5","key":"11100_CR206","doi-asserted-by":"publisher","first-page":"3895","DOI":"10.1007\/s00521-021-06651-x","volume":"34","author":"AM Roy","year":"2022","unstructured":"Roy AM, Bose R, Bhaduri J (2022) A fast accurate fine-grain object detection model based on YOLOv4 deep neural network. Neural Comput Appl 34(5):3895\u20133921. https:\/\/doi.org\/10.1007\/s00521-021-06651-x","journal-title":"Neural Comput Appl"},{"key":"11100_CR207","doi-asserted-by":"crossref","unstructured":"Saari H, Akuj\u00e4rvi A, Holmlund C, Ojanen H, Kaivosoja J, Nissinen A, Niemel\u00e4inen O (2017) Visible, very near IR and short-wave IR hyperspectral drone imaging system for agriculture and natural water applications. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42:165\u2013170","DOI":"10.5194\/isprs-archives-XLII-3-W3-165-2017"},{"key":"11100_CR208","doi-asserted-by":"publisher","unstructured":"Sahu P, Chug A, Singh AP, Singh D, Singh RP (2021) Challenges and issues in plant disease detection using deep learning. In: Dua M, Jain A (eds) Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security. IGI Global, pp 56\u201374. https:\/\/doi.org\/10.4018\/978-1-7998-3299-7.ch004","DOI":"10.4018\/978-1-7998-3299-7.ch004"},{"key":"11100_CR209","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.3390\/plants9111451","volume":"9","author":"MH Saleem","year":"2020","unstructured":"Saleem MH, Khanchi S, Potgieter J, Arif KM (2020a) Image-based plant disease identification by deep learning meta-architectures. Plants 9:1451","journal-title":"Plants"},{"key":"11100_CR210","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1039\/c9pp00368a","volume":"19","author":"M Saleem","year":"2020","unstructured":"Saleem M, Atta BM, Ali Z, Bilal M (2020b) Laser-induced fluorescence spectroscopy for early disease detection in grapefruit plants. Photochemical Photobiological Sci 19:713\u2013721","journal-title":"Photochemical Photobiological Sci"},{"key":"11100_CR211","first-page":"27","volume":"22","author":"G Sambasivam","year":"2021","unstructured":"Sambasivam G, Opiyo GD (2021) A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inf J 22:27\u201334","journal-title":"Egypt Inf J"},{"key":"11100_CR212","doi-asserted-by":"crossref","first-page":"944","DOI":"10.3390\/s18040944","volume":"18","author":"J Sandino","year":"2018","unstructured":"Sandino J, Pegg G, Gonzalez F, Smith G (2018) Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence. Sensors 18:944","journal-title":"Sensors"},{"key":"11100_CR213","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.3390\/s130202117","volume":"13","author":"S Sankaran","year":"2013","unstructured":"Sankaran S, Maja JM, Buchanon S, Ehsani R (2013) Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques. Sensors 13:2117\u20132130","journal-title":"Sensors"},{"key":"11100_CR214","first-page":"1","volume":"1","author":"Y Sasaki","year":"2007","unstructured":"Sasaki Y (2007) The truth of the F-measure. Teach Tutor Mater 1:1\u20135","journal-title":"Teach Tutor Mater"},{"key":"11100_CR215","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1186\/1471-2105-13-171","volume":"13","author":"M Schikora","year":"2012","unstructured":"Schikora M, Neupane B, Madhogaria S et al (2012) An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium. BMC Bioinformatics 13:171","journal-title":"BMC Bioinformatics"},{"key":"11100_CR216","doi-asserted-by":"crossref","first-page":"615","DOI":"10.3390\/agronomy10050615","volume":"10","author":"H Schoofs","year":"2020","unstructured":"Schoofs H, Delalieux S, Deckers T, Bylemans D (2020) Fire blight monitoring in pear orchards by unmanned airborne vehicles (UAV) systems carrying spectral sensors. Agronomy 10:615","journal-title":"Agronomy"},{"key":"11100_CR217","doi-asserted-by":"crossref","unstructured":"Sembiring A, Away Y, Arnia F, Muharar R (2021) Development of concise convolutional neural network for tomato plant disease classification based on leaf images. Journal of Physics: Conference Series 1845:012009","DOI":"10.1088\/1742-6596\/1845\/1\/012009"},{"key":"11100_CR218","doi-asserted-by":"crossref","first-page":"105527","DOI":"10.1016\/j.compag.2020.105527","volume":"175","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature-based rice leaf disease identification using support vector machine. Comput Electron Agric 175:105527","journal-title":"Comput Electron Agric"},{"key":"11100_CR219","doi-asserted-by":"publisher","unstructured":"Shewale MV, Daruwala RD (2023) High performance deep learning architecture for early detection and classification of plant leaf disease. J Agric Food Res 14. https:\/\/doi.org\/10.1016\/j.jafr.2023.100675","DOI":"10.1016\/j.jafr.2023.100675"},{"key":"11100_CR220","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.biosystemseng.2020.03.016","volume":"194","author":"J Shin","year":"2020","unstructured":"Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A (2020) Effect of directional augmentation using supervised machine learning technologies: a case study of strawberry powdery mildew detection. Biosyst Eng 194:49\u201360","journal-title":"Biosyst Eng"},{"key":"11100_CR221","doi-asserted-by":"publisher","first-page":"1158933","DOI":"10.3389\/fpls.2023.1158933","volume":"14","author":"M Shoaib","year":"2023","unstructured":"Shoaib M, Shah B, Ali A, Ullah A, Alenezi F, Gechev T, Hussain T, Ali F (2023) An advanced deep learning models-based plant disease detection: a review of recent research. Front Plant Sci 14:1158933. https:\/\/doi.org\/10.3389\/fpls.2023.1158933","journal-title":"Front Plant Sci"},{"key":"11100_CR222","doi-asserted-by":"crossref","unstructured":"Shrivastava V, Pradhan M, Minz S, Thakur M (2019) Rice plant disease classification using transfer learning of deep convolution neural network. ISPRS - International archives of the photogrammetry, remote sensing and spatial Information sciences XLII. \u20133\/W6:631\u2013635","DOI":"10.5194\/isprs-archives-XLII-3-W6-631-2019"},{"key":"11100_CR223","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compag.2017.09.038","volume":"148","author":"M Shuaibu","year":"2018","unstructured":"Shuaibu M, Lee WS, Schueller J, Gader P, Hong YK, Kim S (2018) Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Comput Electron Agric 148:45\u201353","journal-title":"Comput Electron Agric"},{"key":"11100_CR224","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/jimaging5050052","volume":"5","author":"A Signoroni","year":"2019","unstructured":"Signoroni A, Savardi M, Baronio A, Benini S (2019) Deep learning meets hyperspectral image analysis: a multidisciplinary review. J Imaging 5:52","journal-title":"J Imaging"},{"key":"11100_CR225","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.tplants.2018.07.004","volume":"23","author":"A Singh","year":"2018","unstructured":"Singh A, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: Trends and future perspectives. Trends Plant Sci 23:883\u2013898","journal-title":"Trends Plant Sci"},{"key":"11100_CR226","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.aiia.2020.10.002","volume":"4","author":"V Singh","year":"2019","unstructured":"Singh V, Sharma N, Singh S (2019) A review of imaging techniques for plant disease detection. Artif Intell Agric 4:229\u2013242. https:\/\/doi.org\/10.1016\/j.aiia.2020.10.002","journal-title":"Artif Intell Agric"},{"key":"11100_CR227","doi-asserted-by":"publisher","unstructured":"Sivakumar ANV, Li J, Scott S, Psota E, Jhala AJ, Luck JD, Shi Y (2020) Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sens 12(13). https:\/\/doi.org\/10.3390\/rs12132136","DOI":"10.3390\/rs12132136"},{"key":"11100_CR228","doi-asserted-by":"crossref","unstructured":"Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016:3289801","DOI":"10.1155\/2016\/3289801"},{"key":"11100_CR229","doi-asserted-by":"crossref","unstructured":"Smigaj M, Gaulton R, Barr SL, Su\u00e1rez JC (2015) UAV-borne thermal imaging for forest health monitoring: Detection of disease-induced canopy temperature increase. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 40:349\u2013354","DOI":"10.5194\/isprsarchives-XL-3-W3-349-2015"},{"key":"11100_CR230","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/s40333-016-0049-0","volume":"8","author":"X Song","year":"2016","unstructured":"Song X, Zhang G, Liu F, Li D, Zhao Y, Yang J (2016) Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J Arid Land 8:734\u2013748","journal-title":"J Arid Land"},{"key":"11100_CR231","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.3390\/rs11192209","volume":"11","author":"EL Stewart","year":"2019","unstructured":"Stewart EL, Wiesner-Hanks T, Kaczmar N et al (2019) Quantitative phenotyping of Northern Leaf Blight in UAV images using deep learning. Remote Sens 11:2209","journal-title":"Remote Sens"},{"key":"11100_CR232","unstructured":"Su W, Zhu X, Cao Y, Li B, Lu L, Wei F, Dai J (2019) VL-BERT: pre-training of generic visual-linguistic representations. ArXiv. https:\/\/arxiv.org\/abs\/1908.08530"},{"key":"11100_CR233","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.biosystemseng.2016.04.010","volume":"148","author":"R Sugiura","year":"2016","unstructured":"Sugiura R, Tsuda S, Tamiya S, Itoh A, Nishiwaki K, Murakami N, Shibuya Y, Hirafuji M, Nuske S (2016) Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst Eng 148:1\u201310. https:\/\/doi.org\/10.1016\/j.biosystemseng.2016.04.010","journal-title":"Biosyst Eng"},{"key":"11100_CR234","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1305-6","volume":"43","author":"SK Sundararajan","year":"2019","unstructured":"Sundararajan SK, Sankaragomathi B, Priya DS (2019) Deep belief CNN feature representation-based content-based image retrieval for medical images. J Med Syst 43:1\u20139","journal-title":"J Med Syst"},{"key":"11100_CR235","doi-asserted-by":"crossref","unstructured":"Suproteem K, Sarkara JD, Ehsanib R, Kumara V (2016) Towards autonomous phytopathology: Outcomes and challenges of citrus greening disease detection through close-range remote sensing. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16\u201320 May 2016","DOI":"10.1109\/ICRA.2016.7487719"},{"key":"11100_CR236","doi-asserted-by":"publisher","first-page":"45377","DOI":"10.1109\/ACCESS.2023.3273317","volume":"11","author":"A Tabbakh","year":"2023","unstructured":"Tabbakh A, Barpanda SS (2023) A deep features extraction model based on the transfer learning model and vision transformer TLMViT for plant disease classification. IEEE Access 11:45377\u201345392. https:\/\/doi.org\/10.1109\/ACCESS.2023.3273317","journal-title":"IEEE Access"},{"key":"11100_CR237","doi-asserted-by":"publisher","unstructured":"Thai HT, Tran-Van NY, Le KH (2021) Artificial cognition for early leaf disease detection using vision transformers. In: Proceedings of the International Conference on Advanced Technologies for Communications (ATC), pp. 33\u201338. https:\/\/doi.org\/10.1109\/ATC52653.2021.9598303","DOI":"10.1109\/ATC52653.2021.9598303"},{"key":"11100_CR238","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-11346-8_43","volume-title":"Computer Vision and Image Processing. CVIP 2021","author":"PS Thakur","year":"2022","unstructured":"Thakur PS, Khanna P, Sheorey T, Ojha A (2022) Vision transformer for plant disease detection: PlantViT. In: Raman B, Murala S, Chowdhury A, Dhall A, Goyal P (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-11346-8_43"},{"key":"11100_CR239","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","volume":"125","author":"S Thomas","year":"2018","unstructured":"Thomas S, Kuska MT, Bohnenkamp D, Brugger A, Alisaac E, Wahabzada M, Behmann J, Mahlein AK (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J Plant Dis Prot 125:5\u201320","journal-title":"J Plant Dis Prot"},{"key":"11100_CR240","first-page":"7630926","volume":"2019","author":"Y Tian","year":"2019","unstructured":"Tian Y, Yang G, Wang Z, Li E, Liang Z (2019) Detection of apple lesions in orchards based on deep learning methods of cycle GAN and YOLOv3-dense. J Sens 2019:7630926","journal-title":"J Sens"},{"key":"11100_CR241","first-page":"11","volume":"5","author":"VM Tiwari","year":"2017","unstructured":"Tiwari VM, Tarum G (2017) Plant leaf disease analysis using image processing technique with modified SVM-CS classifier. Int J Eng Manage Technol 5:11\u201317","journal-title":"Int J Eng Manage Technol"},{"key":"11100_CR242","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1016\/j.tplants.2016.10.002","volume":"21","author":"SA Tsaftaris","year":"2016","unstructured":"Tsaftaris SA, Minervini M, Scharr H (2016) Machine learning for plant phenotyping needs image processing. Trends Plant Sci 21:989\u2013991","journal-title":"Trends Plant Sci"},{"key":"11100_CR243","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.3906\/elk-1809-181","volume":"27","author":"M Turkoglu","year":"2019","unstructured":"Turkoglu M, Hanbay D (2019) Plant disease and pest detection using deep learning-based features. Turkish J Electr Eng Comput Sci 27:1636\u20131651","journal-title":"Turkish J Electr Eng Comput Sci"},{"key":"11100_CR244","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.3389\/fpls.2017.02245","volume":"8","author":"J Ubbens","year":"2018","unstructured":"Ubbens J, Stavness I (2018) Corrigendum: deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci 8:2245","journal-title":"Front Plant Sci"},{"key":"11100_CR245","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-8684-2_10","volume-title":"Applications of Computer Vision and Drone Technology in Agriculture 4.0","author":"A Upadhyay","year":"2024","unstructured":"Upadhyay A, Chandel NS, Chakraborty SK (2024) Disease control measures using vision-enabled agricultural robotics. In: Chouhan SS, Singh UP, Jain S (eds) Applications of Computer Vision and Drone Technology in Agriculture 4.0. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-8684-2_10"},{"key":"11100_CR246","doi-asserted-by":"crossref","unstructured":"Valasek J, Thomasson JA, Balota M, Oakes J (2016) Exploratory use of a UAV platform for variety selection in peanut. In: Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, Baltimore, Maryland, 18\u201319 April 2016. 98660F","DOI":"10.1117\/12.2228872"},{"key":"11100_CR247","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: NIPS"},{"key":"11100_CR248","doi-asserted-by":"crossref","unstructured":"Verma S, Chug A, Singh AP, Sharma S, Rajvanshi P (2019) Deep learning-based mobile application for plant disease diagnosis: a proof of concept with a case study on tomato plant. Applications of Image Processing and Soft Computing Systems in Agriculture, pp 242\u2013271","DOI":"10.4018\/978-1-5225-8027-0.ch010"},{"key":"11100_CR249","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/agriculture8050070","volume":"8","author":"N Viljanen","year":"2018","unstructured":"Viljanen N, Honkavaara E, N\u00e4si R, Hakala T, Niemel\u00e4inen O, Kaivosoja J (2018) A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture 8:70","journal-title":"Agriculture"},{"key":"11100_CR250","doi-asserted-by":"crossref","first-page":"105456","DOI":"10.1016\/j.compag.2020.105456","volume":"175","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D, Khanna A, Hassanien AE, Pandey HM (2020) An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput Electron Agric 175:105456","journal-title":"Comput Electron Agric"},{"key":"11100_CR251","unstructured":"Wallelign S, Polceanu M, Buche C (2018) Soybean plant disease identification using convolutional neural network. In: Proc. Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31), pp. 146\u2013151, Melbourne, FL, USA"},{"key":"11100_CR255","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.plaphy.2013.02.004","volume":"66","author":"M Wang","year":"2013","unstructured":"Wang M, Xiong Y, Ling N, Feng X, Zhong Z, Shen Q, Guo S (2013) Detection of the dynamic response of cucumber leaves to fusaric acid using thermal imaging. Plant Physiol Biochem 66:68\u201376","journal-title":"Plant Physiol Biochem"},{"key":"11100_CR254","first-page":"2917536","volume":"2017","author":"G Wang","year":"2017","unstructured":"Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:2917536","journal-title":"Comput Intell Neurosci"},{"key":"11100_CR252","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1038\/s41598-019-40066-y","volume":"9","author":"D Wang","year":"2019","unstructured":"Wang D, Vinson R, Holmes M, Seibel G, Bechar A, Nof S, Tao Y (2019) Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Sci Rep 9:4377","journal-title":"Sci Rep"},{"key":"11100_CR253","doi-asserted-by":"publisher","unstructured":"Wang F, Rao Y, Luo Q, Jin X, Jiang Z, Zhang W, Li S (2022) Practical cucumber leaf disease recognition using improved Swin Transformer and small sample size. Comput Electron Agric 199. https:\/\/doi.org\/10.1016\/j.compag.2022.107163","DOI":"10.1016\/j.compag.2022.107163"},{"key":"11100_CR256","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1186\/s13104-018-3548-6","volume":"11","author":"T Wiesner-Hanks","year":"2018","unstructured":"Wiesner-Hanks T, Stewart EL, Kaczmar N (2018) Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes 11:440","journal-title":"BMC Res Notes"},{"key":"11100_CR257","doi-asserted-by":"publisher","first-page":"98716","DOI":"10.1109\/ACCESS.2020.299700","volume":"8","author":"Q Wu","year":"2020","unstructured":"Wu Q, Chen Y, Meng J (2020) DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access 8:98716\u201398728. https:\/\/doi.org\/10.1109\/ACCESS.2020.299700","journal-title":"IEEE Access"},{"key":"11100_CR258","doi-asserted-by":"crossref","first-page":"20463","DOI":"10.3390\/s150820463","volume":"15","author":"C Xia","year":"2015","unstructured":"Xia C, Wang L, Chung BK, Lee JM (2015) In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation. Sensors 15:20463\u201320479","journal-title":"Sensors"},{"key":"11100_CR259","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compag.2016.12.015","volume":"135","author":"C Xie","year":"2017","unstructured":"Xie C, Yang C, He Y (2017) Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities. Comput Electron Agric 135:154\u2013162","journal-title":"Comput Electron Agric"},{"key":"11100_CR260","doi-asserted-by":"publisher","unstructured":"Xing N, Yeung SH, Cai C, Ng TK, Wang W, Yang K, Yang N, Zhang M, Chen G, Ooi BC (2021) SINGA-Easy: An easy-to-use framework for multi-modal analysis. arXiv. https:\/\/doi.org\/10.1145\/1122445.1122456","DOI":"10.1145\/1122445.1122456"},{"key":"11100_CR261","doi-asserted-by":"crossref","first-page":"101247","DOI":"10.1016\/j.ecoinf.2021.101247","volume":"61","author":"S Yadav","year":"2021","unstructured":"Yadav S, Sengar N, Singh A, Singh A, Dutta MK (2021) Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecol Inform 61:101247","journal-title":"Ecol Inform"},{"key":"11100_CR264","doi-asserted-by":"crossref","first-page":"6","DOI":"10.18088\/ejbmr.3.1.2017.pp6-9","volume":"3","author":"X Yang","year":"2017","unstructured":"Yang X, Guo T (2017) Machine learning in plant disease research. Eur J Biomed Res 3:6\u20139","journal-title":"Eur J Biomed Res"},{"issue":"8","key":"11100_CR262","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1587\/transinf.2018EDP7330","volume":"102","author":"D Yang","year":"2019","unstructured":"Yang D, Li S, Peng Z, Wang P, Wang J, Yang H (2019a) MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE Trans Inf Syst 102(8):1526\u20131536","journal-title":"IEICE Trans Inf Syst"},{"key":"11100_CR263","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1002\/jsfa.9564","volume":"99","author":"N Yang","year":"2019","unstructured":"Yang N, Yuan M, Wang P, Zhang R, Sun J, Mao H (2019b) Tea diseases detection based on fast infrared thermal image processing technology. J Sci Food Agric 99:3459\u20133466","journal-title":"J Sci Food Agric"},{"key":"11100_CR265","doi-asserted-by":"crossref","first-page":"938","DOI":"10.3390\/rs12060938","volume":"12","author":"H Ye","year":"2020","unstructured":"Ye H, Huang W, Huang S, Cui B, Dong Y, Guo A, Ren Y, Jin Y (2020) Recognition of banana fusarium wilt based on UAV remote sensing. Remote Sens 12:938","journal-title":"Remote Sens"},{"key":"11100_CR267","doi-asserted-by":"publisher","DOI":"10.3390\/rs6010064","author":"K Yu","year":"2013","unstructured":"Yu K, Leufen G, Hunsche M, Noga G, Chen X, Bareth G (2013) Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sens. https:\/\/doi.org\/10.3390\/rs6010064","journal-title":"Remote Sens"},{"key":"11100_CR266","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2018.01195","author":"K Yu","year":"2018","unstructured":"Yu K, Anderegg J, Mikaberidze A, Karisto P, Mascher F, McDonald BA et al (2018) Hyperspectral canopy sensing of wheat septoria tritici blotch disease. Front Plant Sci. https:\/\/doi.org\/10.3389\/fpls.2018.01195","journal-title":"Front Plant Sci"},{"key":"11100_CR268","doi-asserted-by":"publisher","first-page":"100650","DOI":"10.1016\/j.iot.2022.100650","volume":"21","author":"S Yu","year":"2023","unstructured":"Yu S, Xie L, Huang Q (2023) Inception convolutional vision transformers for plant disease identification. Internet Things 21:100650. https:\/\/doi.org\/10.1016\/j.iot.2022.100650","journal-title":"Internet Things"},{"key":"11100_CR269","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13007-015-0078-2","volume":"11","author":"M Zaman-Allah","year":"2015","unstructured":"Zaman-Allah M, Vergara O, Araus JL, Tarekegne A, Magorokosho C, Zarco-Tejada PJ, Hornero A, Alba AH, Das B, Craufurd P et al (2015) Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11:35","journal-title":"Plant Methods"},{"key":"11100_CR270","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D (2018a) Detection of rice sheath blight using an unmanned aerial system with high-resolution colour and multispectral imaging. PLoS ONE 13","DOI":"10.1371\/journal.pone.0187470"},{"key":"11100_CR271","doi-asserted-by":"crossref","unstructured":"Zhang K, Wu Q, Liu A, Meng X (2018b) Can deep learning identify tomato leaf disease? Advances in Multimedia 2018:6710865","DOI":"10.1155\/2018\/6710865"},{"key":"11100_CR274","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2844405","author":"X Zhang","year":"2018","unstructured":"Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018c) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2018.2844405","journal-title":"IEEE Access"},{"key":"11100_CR273","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.3390\/rs11131554","volume":"11","author":"X Zhang","year":"2019","unstructured":"Zhang X, Han L, Dong Y, Shi Y, Huang W, Han L, Gonz\u00e1lez-Moo P, Ma H, Ye H, Sobeih T (2019) A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens 11:1554","journal-title":"Remote Sens"},{"key":"11100_CR275","unstructured":"Zhang Z, Yan Y, Dai X, Zhou D, Gai K (2021) Multi-modal pre-training for dense video captioning. https:\/\/arxiv.org\/pdf\/2103.06561.pdf"},{"key":"11100_CR272","doi-asserted-by":"publisher","unstructured":"Zhang L, Zhou G, Lu C, Chen A, Wang Y, Li L, Cai W (2022) MMDGAN: a fusion data augmentation method for tomato-leaf disease identification. Applied Soft Computing 123. Elsevier Ltd. https:\/\/doi.org\/10.1016\/j.asoc.2022.108969.","DOI":"10.1016\/j.asoc.2022.108969"},{"key":"11100_CR278","doi-asserted-by":"crossref","unstructured":"Zhao Y, Gu Y, Qin F, Li X, Ma Z, Zhao L, Li J, Cheng P, Pan Y, Wang H (2017) Application of near-infrared spectroscopy to quantitatively determine relative content of Puccinia striiformis f. sp. tritici DNA in wheat leaves in incubation period. J. Spectrosc. 2017:9740295","DOI":"10.1155\/2017\/9740295"},{"key":"11100_CR276","first-page":"55","volume":"12","author":"H Zhao","year":"2020","unstructured":"Zhao H, Jia J, Koltun V (2020) Exploring self-attention for image recognition. CVPR 12:55\u201363","journal-title":"CVPR"},{"key":"11100_CR277","doi-asserted-by":"publisher","unstructured":"Zhao Y, Sun C, Xu X, Chen J (2022) RIC-Net: a plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism. Comput Electron Agric 193. https:\/\/doi.org\/10.1016\/j.compag.2021.106644","DOI":"10.1016\/j.compag.2021.106644"},{"key":"11100_CR279","doi-asserted-by":"crossref","first-page":"105576","DOI":"10.1016\/j.compag.2020.105576","volume":"175","author":"J Zhou","year":"2020","unstructured":"Zhou J, Zhou J, Ye H, Ali ML, Nguyen HT, Chen P (2020) Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Comput Electron Agric 175:105576","journal-title":"Comput Electron Agric"},{"key":"11100_CR281","first-page":"32","volume":"11","author":"N Zhu","year":"2018","unstructured":"Zhu N, Liu X, Liu Z, Hu K, Wang Y, Tan J, Huang M, Zhu Q, Ji X, Jiang Y (2018) Deep learning for smart agriculture: concepts, tools, applications, and opportunities. Int J Agric Biol Eng 11:32\u201344","journal-title":"Int J Agric Biol Eng"},{"key":"11100_CR280","doi-asserted-by":"publisher","first-page":"105603","DOI":"10.1016\/j.compag.2020.105603","volume":"175","author":"F Zhu","year":"2020","unstructured":"Zhu F, He M, Zheng Z (2020) Data augmentation using improved cDCGAN for plant vigor rating. Comput Electron Agric 175:105603. https:\/\/doi.org\/10.1016\/j.compag.2020.105603","journal-title":"Comput Electron Agric"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-11100-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-11100-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-11100-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T11:32:14Z","timestamp":1738582334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-11100-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":281,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["11100"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-11100-x","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"29 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"92"}}