{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:08:57Z","timestamp":1781017737659,"version":"3.54.1"},"reference-count":196,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18392-9","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T06:02:10Z","timestamp":1707199330000},"page":"70961-71000","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Intelligent detection for sustainable agriculture: A review of IoT-based embedded systems, cloud platforms, DL, and ML for plant disease detection"],"prefix":"10.1007","volume":"83","author":[{"given":"Abdennabi","family":"Morchid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marouane","family":"Marhoun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachid","family":"El Alami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9345-177X","authenticated-orcid":false,"given":"Bensalem","family":"Boukili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"18392_CR1","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s12571-012-0200-5","volume":"4","author":"S Savary","year":"2012","unstructured":"Savary S, Ficke A, Aubertot J-N, Hollier C (2012) Crop losses due to diseases and their implications for global food production losses and food security. Food Sec 4:519\u2013537. https:\/\/doi.org\/10.1007\/s12571-012-0200-5","journal-title":"Food Sec"},{"key":"18392_CR2","unstructured":"FAO (2015) Climate change and food security: risks and responses. FAO, Rome, Italy. https:\/\/www.fao.org\/documents\/card\/en?details=82129a98-8338-45e5-a2cd-8eda4184550f\/"},{"key":"18392_CR3","unstructured":"Shalaby MY, Al-Zahrani KH, Baig MB et al (2011) Threats and challenges to sustainable agriculture and rural development in egypt: implications for agricultural extension. J Anim Plant Sci 21(3):581\u2013588. https:\/\/thejaps.org.pk\/docs\/21-3\/25.pdf"},{"key":"18392_CR4","doi-asserted-by":"publisher","first-page":"101039","DOI":"10.1016\/j.funeco.2021.101039","volume":"50","author":"TS Suryanarayanan","year":"2021","unstructured":"Suryanarayanan TS, Shaanker RU (2021) Can fungal endophytes fast-track plant adaptations to climate change? Fungal Ecol 50:101039. https:\/\/doi.org\/10.1016\/j.funeco.2021.101039","journal-title":"Fungal Ecol"},{"key":"18392_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1038\/scientificamerican0387-78","volume":"256","author":"JF Power","year":"1987","unstructured":"Power JF, Follett RF (1987) Monoculture. Sci Am 256:78\u201387","journal-title":"Sci Am"},{"key":"18392_CR6","doi-asserted-by":"publisher","first-page":"160","DOI":"10.5897\/JPBCS2014.0467","volume":"6","author":"RK Mishra","year":"2014","unstructured":"Mishra RK, Jaiswal RK, Kumar D et al (2014) Management of major diseases and insect pests of onion and garlic: A comprehensive review. JPBCS 6:160\u2013170. https:\/\/doi.org\/10.5897\/JPBCS2014.0467","journal-title":"JPBCS"},{"key":"18392_CR7","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wcc.395","volume":"7","author":"P Slavin","year":"2016","unstructured":"Slavin P (2016) Climate and famines: a historical reassessment. WIREs Clim Change 7:433\u2013447. https:\/\/doi.org\/10.1002\/wcc.395","journal-title":"WIREs Clim Change"},{"key":"18392_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s43154-020-00004-7","volume":"1","author":"P Mishra","year":"2020","unstructured":"Mishra P, Polder G, Vilfan N (2020) Close range spectral imaging for disease detection in plants using autonomous platforms: A review on recent studies. Curr Robot Rep 1:43\u201348. https:\/\/doi.org\/10.1007\/s43154-020-00004-7","journal-title":"Curr Robot Rep"},{"key":"18392_CR9","doi-asserted-by":"publisher","first-page":"52181","DOI":"10.1109\/ACCESS.2020.2980310","volume":"8","author":"L Liu","year":"2020","unstructured":"Liu L, Dong Y, Huang W et al (2020) A disease index for efficiently detecting wheat fusarium head blight using Sentinel-2 multispectral imagery. IEEE Access 8:52181\u201352191. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980310","journal-title":"IEEE Access"},{"key":"18392_CR10","doi-asserted-by":"publisher","first-page":"e3958","DOI":"10.1002\/ett.3958","volume":"31","author":"S Terence","year":"2020","unstructured":"Terence S, Purushothaman G (2020) Systematic review of internet of things in smart farming. Trans Emerg Telecommun Technol 31:e3958. https:\/\/doi.org\/10.1002\/ett.3958","journal-title":"Trans Emerg Telecommun Technol"},{"key":"18392_CR11","doi-asserted-by":"publisher","first-page":"21219","DOI":"10.1109\/ACCESS.2022.3152544","volume":"10","author":"S Qazi","year":"2022","unstructured":"Qazi S, Khawaja BA, Farooq QU (2022) IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends. IEEE Access 10:21219\u201321235. https:\/\/doi.org\/10.1109\/ACCESS.2022.3152544","journal-title":"IEEE Access"},{"key":"18392_CR12","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.3390\/rs13132486","volume":"13","author":"M Ouhami","year":"2021","unstructured":"Ouhami M, Hafiane A, Es-Saady Y et al (2021) Computer vision, IoT and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing 13:2486. https:\/\/doi.org\/10.3390\/rs13132486","journal-title":"Remote Sensing"},{"key":"18392_CR13","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.3390\/app12073396","volume":"12","author":"VK Quy","year":"2022","unstructured":"Quy VK, Hau NV, Anh DV et al (2022) IoT-enabled smart agriculture: Architecture, applications, and challenges. Appl Sci 12:3396. https:\/\/doi.org\/10.3390\/app12073396","journal-title":"Appl Sci"},{"key":"18392_CR14","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":"18392_CR15","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX (2018) Deep learning in agriculture: A survey. Comput Electron Agric 147:70\u201390. https:\/\/doi.org\/10.1016\/j.compag.2018.02.016","journal-title":"Comput Electron Agric"},{"key":"18392_CR16","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/ACCESS.2021.3138160","volume":"10","author":"E-T Bouali","year":"2022","unstructured":"Bouali E-T, Abid MR, Boufounas E-M et al (2022) Renewable energy integration into cloud & IoT-based smart agriculture. IEEE Access 10:1175\u20131191. https:\/\/doi.org\/10.1109\/ACCESS.2021.3138160","journal-title":"IEEE Access"},{"key":"18392_CR17","doi-asserted-by":"publisher","first-page":"5866","DOI":"10.3390\/s22155866","volume":"22","author":"M Cruz","year":"2022","unstructured":"Cruz M, Mafra S, Teixeira E, Figueiredo F (2022) Smart strawberry farming using edge computing and IoT. Sensors 22:5866. https:\/\/doi.org\/10.3390\/s22155866","journal-title":"Sensors"},{"key":"18392_CR18","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-030-73882-2_40","volume-title":"Digital Technologies and Applications","author":"A Morchid","year":"2021","unstructured":"Morchid A, El Alaoui M, El Alami R et al (2021) Design and realization of fire safety system for controlling and monitoring a siren using arduino uno. In: Motahhir S, Bossoufi B (eds) Digital Technologies and Applications. Springer International Publishing, Cham, pp 433\u2013445"},{"key":"18392_CR19","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/978-3-031-01942-5_73","volume-title":"Digital Technologies and Applications","author":"A Morchid","year":"2022","unstructured":"Morchid A, El Alami R, Qjidaa H et al (2022) Fire safety system implementation for controlling and monitoring a siren in smart farm using gas sensor and flame sensor. In: Motahhir S, Bossoufi B (eds) Digital Technologies and Applications. Springer International Publishing, Cham, pp 733\u2013742"},{"key":"18392_CR20","doi-asserted-by":"publisher","first-page":"100161","DOI":"10.1016\/j.iot.2020.100161","volume":"9","author":"M Mahbub","year":"2020","unstructured":"Mahbub M (2020) A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. Internet Things 9:100161. https:\/\/doi.org\/10.1016\/j.iot.2020.100161","journal-title":"Internet Things"},{"key":"18392_CR21","doi-asserted-by":"publisher","first-page":"107936","DOI":"10.1016\/j.cie.2022.107936","volume":"165","author":"A Sharma","year":"2022","unstructured":"Sharma A, Georgi M, Tregubenko M et al (2022) Enabling smart agriculture by implementing artificial intelligence and embedded sensing. Comput Ind Eng 165:107936. https:\/\/doi.org\/10.1016\/j.cie.2022.107936","journal-title":"Comput Ind Eng"},{"key":"18392_CR22","doi-asserted-by":"publisher","first-page":"55","DOI":"10.4018\/IJAEIS.20210101.oa4","volume":"12","author":"H Pang","year":"2021","unstructured":"Pang H, Zheng Z, Zhen T, Sharma A (2021) Smart farming: An approach for disease detection implementing IoT and image processing. Int J Agric Environ Inf Syst (IJAEIS) 12:55\u201367. https:\/\/doi.org\/10.4018\/IJAEIS.20210101.oa4","journal-title":"Int J Agric Environ Inf Syst (IJAEIS)"},{"key":"18392_CR23","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.patcog.2017.05.015","journal-title":"Pattern Recogn"},{"key":"18392_CR24","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.tplants.2016.10.002","journal-title":"Trends Plant Sci"},{"key":"18392_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-40605-9_1","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"A Fuentes","year":"2020","unstructured":"Fuentes A, Yoon S, Park DS (2020) Deep learning-based techniques for plant diseases recognition in real-field scenarios. In: Blanc-Talon J, Delmas P, Philips W et al (eds) Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, Cham, pp 3\u201314"},{"key":"18392_CR26","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R et al (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18392_CR27","doi-asserted-by":"publisher","unstructured":"Shakya AD, Subarna (2018) Image-based plant disease detection with deep learning. International Journal of Computer Trends and Technology 61(1):26\u201329. https:\/\/doi.org\/10.14445\/22312803\/IJCTT-V61P105","DOI":"10.14445\/22312803\/IJCTT-V61P105"},{"key":"18392_CR28","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s40860-020-00127-w","volume":"7","author":"SS Goel","year":"2021","unstructured":"Goel SS, Goel A, Kumar M, Molt\u00f3 G (2021) A review of internet of things: Qualifying technologies and boundless horizon. J Reliable Intell Environ 7:23\u201333. https:\/\/doi.org\/10.1007\/s40860-020-00127-w","journal-title":"J Reliable Intell Environ"},{"key":"18392_CR29","doi-asserted-by":"publisher","first-page":"100674","DOI":"10.1016\/j.iot.2022.100674","volume":"21","author":"S Iftikhar","year":"2023","unstructured":"Iftikhar S, Gill SS, Song C et al (2023) AI-based fog and edge computing: A systematic review, taxonomy and future directions. Internet Things 21:100674. https:\/\/doi.org\/10.1016\/j.iot.2022.100674","journal-title":"Internet Things"},{"key":"18392_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2019.06.006","volume":"143","author":"M Kumar","year":"2019","unstructured":"Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1\u201333. https:\/\/doi.org\/10.1016\/j.jnca.2019.06.006","journal-title":"J Netw Comput Appl"},{"key":"18392_CR31","doi-asserted-by":"publisher","unstructured":"Samriya MKJ, Kumar M, Kumar J (2023) Smart and reliable agriculture application using IoT-enabled fog-cloud platform. In: Industrial Reliability and Safety Engineering. CRC Press. https:\/\/doi.org\/10.1201\/9781003140092-4","DOI":"10.1201\/9781003140092-4"},{"key":"18392_CR32","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-981-13-1501-5_19","volume-title":"Emerging technologies in data mining and information security","author":"P Niveditha","year":"2019","unstructured":"Niveditha P, Gururaj HL, Janhavi V (2019) An analysis of various techniques for leaf disease prediction. In: Abraham A, Dutta P, Mandal JK et al (eds) Emerging technologies in data mining and information security. Springer, Singapore, pp 229\u2013239"},{"key":"18392_CR33","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-1-4615-7294-7_2","volume-title":"Introduction to plant diseases: Identification and management","author":"GB Lucas","year":"1992","unstructured":"Lucas GB, Campbell CL, Lucas LT (1992) Causes of plant diseases. In: Lucas GB, Campbell CL, Lucas LT (eds) Introduction to plant diseases: Identification and management. Springer, US, Boston, MA, pp 9\u201314"},{"key":"18392_CR34","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-981-10-5514-0_5","volume-title":"Plant microbiome: Stress response","author":"LKT Al-Ani","year":"2018","unstructured":"Al-Ani LKT (2018) Trichoderma: beneficial role in sustainable agriculture by plant disease management. In: Egamberdieva D, Ahmad P (eds) Plant microbiome: Stress response. Springer, Singapore, pp 105\u2013126"},{"key":"18392_CR35","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11831-018-9255-6","volume":"26","author":"S Kaur","year":"2019","unstructured":"Kaur S, Pandey S, Goel S (2019) Plants disease identification and classification through leaf images: A survey. Arch Computat Methods Eng 26:507\u2013530. https:\/\/doi.org\/10.1007\/s11831-018-9255-6","journal-title":"Arch Computat Methods Eng"},{"key":"18392_CR36","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/agriculture12010009","volume":"12","author":"H Orchi","year":"2022","unstructured":"Orchi H, Sadik M, Khaldoun M (2022) On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey. Agriculture 12:9. https:\/\/doi.org\/10.3390\/agriculture12010009","journal-title":"Agriculture"},{"key":"18392_CR37","doi-asserted-by":"publisher","first-page":"7243","DOI":"10.1109\/JIOT.2021.3097379","volume":"9","author":"G Delnevo","year":"2022","unstructured":"Delnevo G, Girau R, Ceccarini C, Prandi C (2022) A deep learning and social IoT approach for plants disease prediction toward a sustainable agriculture. IEEE Internet Things J 9:7243\u20137250. https:\/\/doi.org\/10.1109\/JIOT.2021.3097379","journal-title":"IEEE Internet Things J"},{"key":"18392_CR38","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1109\/JIOT.2019.2947624","volume":"7","author":"W-L Chen","year":"2020","unstructured":"Chen W-L, Lin Y-B, Ng F-L et al (2020) RiceTalk: Rice blast detection using internet of things and artificial intelligence technologies. IEEE Internet Things J 7:1001\u20131010. https:\/\/doi.org\/10.1109\/JIOT.2019.2947624","journal-title":"IEEE Internet Things J"},{"key":"18392_CR39","doi-asserted-by":"publisher","unstructured":"Truong T, Dinh A, Wahid K (2017) An IoT environmental data collection system for fungal detection in crop fields. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). pp 1\u20134.\u00a0https:\/\/doi.org\/10.1109\/CCECE.2017.7946787","DOI":"10.1109\/CCECE.2017.7946787"},{"key":"18392_CR40","doi-asserted-by":"publisher","unstructured":"Singh T, Singh D, Bedi SS (2021) Monitoring and detecting plant diseases using cloud-based internet of things. Integration and implementation of the internet of things through cloud computing: 217\u201335. https:\/\/doi.org\/10.4018\/978-1-7998-6981-8.ch011","DOI":"10.4018\/978-1-7998-6981-8.ch011"},{"key":"18392_CR41","doi-asserted-by":"publisher","unstructured":"Ale L, Sheta A, Li L et al (2019) Deep learning based plant disease detection for smart agriculture. In: 2019 IEEE Globecom Workshops (GC Wkshps). pp 1\u20136. https:\/\/doi.org\/10.1109\/GCWkshps45667.2019.9024439","DOI":"10.1109\/GCWkshps45667.2019.9024439"},{"key":"18392_CR42","doi-asserted-by":"crossref","unstructured":"Kitpo N, Inoue M (2018) Early rice disease detection and position mapping system using drone and IoT architecture. In: 2018 12th South East Asian Technical University Consortium (SEATUC). pp 1\u20135","DOI":"10.1109\/SEATUC.2018.8788863"},{"key":"18392_CR43","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/jlpea12020024","volume":"12","author":"A Musa","year":"2022","unstructured":"Musa A, Hassan M, Hamada M, Aliyu F (2022) Low-power deep learning model for plant disease detection for smart-hydroponics using knowledge distillation techniques. J Low Power Electron Applic 12:24. https:\/\/doi.org\/10.3390\/jlpea12020024","journal-title":"J Low Power Electron Applic"},{"key":"18392_CR44","doi-asserted-by":"publisher","first-page":"105951","DOI":"10.1016\/j.compag.2020.105951","volume":"181","author":"V Gonzalez-Huitron","year":"2021","unstructured":"Gonzalez-Huitron V, Le\u00f3n-Borges JA, Rodriguez-Mata AE et al (2021) Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Comput Electron Agric 181:105951. https:\/\/doi.org\/10.1016\/j.compag.2020.105951","journal-title":"Comput Electron Agric"},{"key":"18392_CR45","unstructured":"Dubey AMS (2020) Agricultural plant disease detection and identification. International Journal of Electrical Engineering and Technology 11(3):354\u2013363. https:\/\/ssrn.com\/abstract=3636681"},{"key":"18392_CR46","unstructured":"Prabha DR, Swaminathan R, Chaitanya K, Sultana WR (2019) Arduino based smart irrigation system and plant leaf disease detection using MATLAB. 0(12):37\u201347. https:\/\/ssrn.com\/abstract=3525954"},{"key":"18392_CR47","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s11277-017-5092-4","volume":"102","author":"S Aasha Nandhini","year":"2018","unstructured":"Aasha Nandhini S, Hemalatha R, Radha S, Indumathi K (2018) Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Pers Commun 102:725\u2013740. https:\/\/doi.org\/10.1007\/s11277-017-5092-4","journal-title":"Wireless Pers Commun"},{"key":"18392_CR48","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17:2022. https:\/\/doi.org\/10.3390\/s17092022","journal-title":"Sensors"},{"key":"18392_CR49","doi-asserted-by":"publisher","unstructured":"Musa A, Hamada M, Aliyu FM, Hassan M (2021) An intelligent plant dissease detection system for smart hydroponic using convolutional neural network. In: 2021 IEEE 14th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC). pp 345\u2013351.\u00a0https:\/\/doi.org\/10.1109\/MCSoC51149.2021.00058","DOI":"10.1109\/MCSoC51149.2021.00058"},{"key":"18392_CR50","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s12652-020-02051-6","volume":"12","author":"M Mishra","year":"2021","unstructured":"Mishra M, Choudhury P, Pati B (2021) Modified ride-NN optimizer for the IoT based plant disease detection. J Ambient Intell Hum Comput 12:691\u2013703. https:\/\/doi.org\/10.1007\/s12652-020-02051-6","journal-title":"J Ambient Intell Hum Comput"},{"key":"18392_CR51","doi-asserted-by":"publisher","first-page":"102509","DOI":"10.1016\/j.jag.2021.102509","volume":"104","author":"Y Oishi","year":"2021","unstructured":"Oishi Y, Habaragamuwa H, Zhang Y et al (2021) Automated abnormal potato plant detection system using deep learning models and portable video cameras. Int J Appl Earth Obs Geoinf 104:102509. https:\/\/doi.org\/10.1016\/j.jag.2021.102509","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"18392_CR52","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1007\/s00371-021-02164-9","volume":"38","author":"R Gajjar","year":"2022","unstructured":"Gajjar R, Gajjar N, Thakor VJ et al (2022) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis Comput 38:2923\u20132938. https:\/\/doi.org\/10.1007\/s00371-021-02164-9","journal-title":"Vis Comput"},{"key":"18392_CR53","unstructured":"Comprendre l\u2019internet des objets | Internet of things | AWS IoT. In: Amazon web services, Inc. https:\/\/aws.amazon.com\/fr\/iot\/. Accessed 3 Oct 2022"},{"key":"18392_CR54","unstructured":"Cloud IoT core. In: Google cloud. https:\/\/cloud.google.com\/iot-core?hl=fr. Accessed 3 Oct 2022"},{"key":"18392_CR55","unstructured":"Azure IoT \u2013 Internet of things platform | Microsoft azure. https:\/\/azure.microsoft.com\/en-us\/solutions\/iot\/. Accessed 3 Oct 2022"},{"key":"18392_CR56","unstructured":"Azure AI platform \u2013 Artificial intelligence service | Microsoft azure. https:\/\/azure.microsoft.com\/en-us\/solutions\/ai\/. Accessed 5 Oct 2022"},{"key":"18392_CR57","unstructured":"Accelerate your operations with IOT. https:\/\/www.oracle.com\/internet-of-things\/. Accessed 31 Jan 2024"},{"key":"18392_CR58","unstructured":"Nguyen D (2018) Firebase with realtime database for IoT applications. In: Konel. https:\/\/medium.com\/konel\/firebase-with-realtime-database-for-iot-applications-e615a7057a48. Accessed 31 Jan 2024"},{"key":"18392_CR59","unstructured":"ThingSpeak for smart farming - ThingSpeak IoT. https:\/\/thingspeak.com\/pages\/smart_farming. Accessed 3 Oct 2022"},{"key":"18392_CR60","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.1016\/j.matpr.2022.03.314","volume":"62","author":"B Gupta","year":"2022","unstructured":"Gupta B, Madan G, Quadir MdA (2022) A smart agriculture framework for IoT based plant decay detection using smart croft algorithm. Mater Today: Proc 62:4758\u20134763. https:\/\/doi.org\/10.1016\/j.matpr.2022.03.314","journal-title":"Mater Today: Proc"},{"key":"18392_CR61","unstructured":"thingsboard ThingsBoard - Open-source IoT Platform. In: ThingsBoard. https:\/\/thingsboard.io\/. Accessed 3 Oct 2022"},{"key":"18392_CR62","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.3390\/plants12112073","volume":"12","author":"Y Liu","year":"2023","unstructured":"Liu Y, Liu J, Cheng W et al (2023) A high-precision plant disease detection method based on a dynamic pruning gate friendly to low-computing platforms. Plants 12:2073. https:\/\/doi.org\/10.3390\/plants12112073","journal-title":"Plants"},{"key":"18392_CR63","doi-asserted-by":"publisher","first-page":"e0243243","DOI":"10.1371\/journal.pone.0243243","volume":"15","author":"A Khan","year":"2020","unstructured":"Khan A, Nawaz U, Ulhaq A, Robinson RW (2020) Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens. PLoS One 15:e0243243. https:\/\/doi.org\/10.1371\/journal.pone.0243243","journal-title":"PLoS One"},{"key":"18392_CR64","doi-asserted-by":"publisher","unstructured":"Chamara N, Islam MD, Bai G (Frank) et al (2022) Ag-IoT for crop and environment monitoring: Past, present, and future. Agric Syst 203:103497. https:\/\/doi.org\/10.1016\/j.agsy.2022.103497","DOI":"10.1016\/j.agsy.2022.103497"},{"key":"18392_CR65","doi-asserted-by":"publisher","first-page":"57952","DOI":"10.1109\/ACCESS.2020.2982443","volume":"8","author":"M Lv","year":"2020","unstructured":"Lv M, Zhou G, He M et al (2020) Maize leaf disease identification based on feature enhancement and DMS-robust alexnet. IEEE Access 8:57952\u201357966. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982443","journal-title":"IEEE Access"},{"key":"18392_CR66","doi-asserted-by":"publisher","unstructured":"Nagaraju T, Malleswari B (2021) Real-time agriculture plant leaf monitoring and disease identification system using raspberry Pi. Math Stat Eng Applic 70:382\u2013399. https:\/\/doi.org\/10.17762\/msea.v70i2.1640","DOI":"10.17762\/msea.v70i2.1640"},{"key":"18392_CR67","doi-asserted-by":"publisher","unstructured":"Jhatial MJ, Shaikh DRA, Arain DRH et al (2023) Azure-based multi-sensor IoT network for smart rice-nursery field. VFAST Trans Softw Eng 11:187\u2013195. https:\/\/doi.org\/10.21015\/vtse.v11i2.1538","DOI":"10.21015\/vtse.v11i2.1538"},{"key":"18392_CR68","doi-asserted-by":"publisher","unstructured":"Yetukuri NK, Maddali K, Jayapandian N (2023) Machine learning based plant disease identification by using hybrid na\u00efve bayes with decision tree algorithm. In: 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). pp 852\u2013858.\u00a0https:\/\/doi.org\/10.1109\/ICSSIT55814.2023.10061099","DOI":"10.1109\/ICSSIT55814.2023.10061099"},{"key":"18392_CR69","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-3-030-97610-1_52","volume-title":"Artificial intelligence in data and big data processing","author":"N Thai-Nghe","year":"2022","unstructured":"Thai-Nghe N, Tri NT, Hoa NH (2022) Deep learning for Rice leaf disease detection in smart agriculture. In: Dang NHT, Zhang Y-D, Tavares JMRS, Chen B-H (eds) Artificial intelligence in data and big data processing. Springer International Publishing, Cham, pp 659\u2013670"},{"key":"18392_CR70","doi-asserted-by":"publisher","unstructured":"Moloo RK, Caleechurn K (2022) An app for fungal disease detection on plants. In: 2022 International Conference for Advancement in Technology (ICONAT). pp 1\u20135. https:\/\/doi.org\/10.1109\/ICONAT53423.2022.9725839","DOI":"10.1109\/ICONAT53423.2022.9725839"},{"key":"18392_CR71","doi-asserted-by":"publisher","unstructured":"S AN, R H, S R et al (2023) A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices. Multimed Tools Appl.\u00a0https:\/\/doi.org\/10.1007\/s11042-023-15442-6","DOI":"10.1007\/s11042-023-15442-6"},{"key":"18392_CR72","doi-asserted-by":"publisher","unstructured":"Rajeshwari T, Harsha Vardhini PA, Manoj Kumar Reddy K et al (2021) Smart agriculture implementation using IoT and leaf disease detection using logistic regression. In: 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE). pp 619\u2013623. https:\/\/doi.org\/10.1109\/RDCAPE52977.2021.9633608","DOI":"10.1109\/RDCAPE52977.2021.9633608"},{"key":"18392_CR73","doi-asserted-by":"crossref","unstructured":"Suneja B, Negi A, Kumar N, Bhardwaj R (2022) Cloud-based tomato plant growth and health monitoring system using IoT. In: 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM). pp 237\u2013243","DOI":"10.1109\/ICIEM54221.2022.9853170"},{"key":"18392_CR74","doi-asserted-by":"publisher","unstructured":"Banerjee A, Lal E, Berlin Hency V (2023) IoT-based plant health monitoring system using CNN and image processing. In: Wiley J, Sons Ltd (eds) Integrated green energy solutions 1:263\u2013290.\u00a0https:\/\/doi.org\/10.1002\/9781119847564.ch18","DOI":"10.1002\/9781119847564.ch18"},{"key":"18392_CR75","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-319-98998-3_19","volume-title":"Advances in computing","author":"H Cadavid","year":"2018","unstructured":"Cadavid H, Garz\u00f3n W, P\u00e9rez A et al (2018) Towards a smart farming platform: From IoT-based crop sensing to data analytics. In: Serrano CJE, Mart\u00ednez-Santos JC (eds) Advances in computing. Springer International Publishing, Cham, pp 237\u2013251"},{"key":"18392_CR76","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.3390\/s20071827","volume":"20","author":"RK Singh","year":"2020","unstructured":"Singh RK, Aernouts M, De Meyer M et al (2020) Leveraging LoRaWAN technology for precision agriculture in greenhouses. Sensors 20:1827. https:\/\/doi.org\/10.3390\/s20071827","journal-title":"Sensors"},{"key":"18392_CR77","doi-asserted-by":"publisher","unstructured":"Amir Alavi S, Rahimian A, Mehran K, Mehr Ardestani J (2018) An IoT-based data collection platform for situational awareness-centric microgrids. In: 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). pp 1\u20134. https:\/\/doi.org\/10.1109\/CCECE.2018.8447718","DOI":"10.1109\/CCECE.2018.8447718"},{"key":"18392_CR78","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. Inf Process Agric 8:27\u201351. https:\/\/doi.org\/10.1016\/j.inpa.2020.04.004","journal-title":"Inf Process Agric"},{"key":"18392_CR79","doi-asserted-by":"publisher","first-page":"e5541859","DOI":"10.1155\/2021\/5541859","volume":"2021","author":"RU Khan","year":"2021","unstructured":"Khan RU, Khan K, Albattah W, Qamar AM (2021) Image-based detection of plant diseases: From classical machine learning to deep learning journey. Wirel Commun Mob Comput 2021:e5541859. https:\/\/doi.org\/10.1155\/2021\/5541859","journal-title":"Wirel Commun Mob Comput"},{"key":"18392_CR80","doi-asserted-by":"publisher","unstructured":"Nachtigall LG, Araujo RM, Nachtigall GR (2016) Classification of apple tree disorders using convolutional neural networks. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). pp 472\u2013476.\u00a0https:\/\/doi.org\/10.1109\/ICTAI.2016.0078","DOI":"10.1109\/ICTAI.2016.0078"},{"key":"18392_CR81","doi-asserted-by":"crossref","unstructured":"Islam M, Dinh A, Wahid K, Bhowmik P (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). pp 1\u20134","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"18392_CR82","doi-asserted-by":"crossref","unstructured":"Francis J, D ASD, K AB (2016) Identification of leaf diseases in pepper plants using soft computing techniques. In: 2016 Conference on Emerging Devices and Smart Systems (ICEDSS). pp 168\u2013173","DOI":"10.1109\/ICEDSS.2016.7587787"},{"key":"18392_CR83","doi-asserted-by":"publisher","unstructured":"Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on Advances in Signal Processing (CASP). pp 175\u2013179. https:\/\/doi.org\/10.1109\/CASP.2016.7746160","DOI":"10.1109\/CASP.2016.7746160"},{"key":"18392_CR84","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1109\/ACCESS.2018.2800685","volume":"6","author":"SS Chouhan","year":"2018","unstructured":"Chouhan SS, Kaul A, Singh UP, Jain S (2018) Bacterial foraging optimization Based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access 6:8852\u20138863. https:\/\/doi.org\/10.1109\/ACCESS.2018.2800685","journal-title":"IEEE Access"},{"key":"18392_CR85","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.inpa.2019.12.002","volume":"7","author":"MH Asad","year":"2020","unstructured":"Asad MH, Bais A (2020) Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Inf Process Agric 7:535\u2013545. https:\/\/doi.org\/10.1016\/j.inpa.2019.12.002","journal-title":"Inf Process Agric"},{"key":"18392_CR86","doi-asserted-by":"publisher","unstructured":"Bresilla K, Perulli GD, Boini A et al (2019) Single-shot convolution neural networks for real-time fruit detection within the tree. Front Plant Sci 10. https:\/\/doi.org\/10.3389\/fpls.2019.00611","DOI":"10.3389\/fpls.2019.00611"},{"key":"18392_CR87","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TII.2018.2875149","volume":"15","author":"MS Hossain","year":"2019","unstructured":"Hossain MS, Al-Hammadi M, Muhammad G (2019) Automatic fruit classification using deep learning for industrial applications. IEEE Trans Ind Inf 15:1027\u20131034. https:\/\/doi.org\/10.1109\/TII.2018.2875149","journal-title":"IEEE Trans Ind Inf"},{"key":"18392_CR88","doi-asserted-by":"publisher","first-page":"122758","DOI":"10.1109\/ACCESS.2019.2938194","volume":"7","author":"F Ren","year":"2019","unstructured":"Ren F, Liu W, Wu G (2019) Feature reuse residual networks for insect Pest recognition. IEEE Access 7:122758\u2013122768. https:\/\/doi.org\/10.1109\/ACCESS.2019.2938194","journal-title":"IEEE Access"},{"key":"18392_CR89","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.biosystemseng.2018.05.013","volume":"172","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018) Factors influencing the use of deep learning for plant disease recognition. Biosys Eng 172:84\u201391. https:\/\/doi.org\/10.1016\/j.biosystemseng.2018.05.013","journal-title":"Biosys Eng"},{"key":"18392_CR90","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A et al (2016) Deep learning for visual understanding: A review. Neurocomputing 187:27\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116","journal-title":"Neurocomputing"},{"key":"18392_CR91","doi-asserted-by":"publisher","unstructured":"Fujita E, Kawasaki Y, Uga H et al (2016) Basic investigation on a robust and practical plant diagnostic system. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). pp 989\u2013992. https:\/\/doi.org\/10.1109\/ICMLA.2016.0178","DOI":"10.1109\/ICMLA.2016.0178"},{"key":"18392_CR92","doi-asserted-by":"publisher","first-page":"e3289801","DOI":"10.1155\/2016\/3289801","volume":"2016","author":"S Sladojevic","year":"2016","unstructured":"Sladojevic S, Arsenovic M, Anderla A et al (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:e3289801. https:\/\/doi.org\/10.1155\/2016\/3289801","journal-title":"Comput Intell Neurosci"},{"key":"18392_CR93","unstructured":"Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Atenbanksysteme f\u00fcr Business, Technologie und Web (BTW 2017) - Workshopband. Bonn: Gesellschaft f\u00fcr Informatik e.V.. PISSN: 1617-5468. 978-3-88579-660-2. pp 79\u201388. Workshop Big (and small) Data in Science and Humanities (BigDS17). Stuttgart. 6.-10. M\u00e4rz 2017. https:\/\/dl.gi.de\/items\/13766147-8092-4f0a-b4e1-8a11a9046bdf"},{"key":"18392_CR94","doi-asserted-by":"publisher","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M (2016) Using deep learning for image-based plant disease detection. Frontiers in plant science 7. https:\/\/doi.org\/10.3389\/fpls.2016.01419","DOI":"10.3389\/fpls.2016.01419"},{"key":"18392_CR95","doi-asserted-by":"publisher","unstructured":"Cruz AC, Luvisi A, De Bellis L, Ampatzidis Y (2017) X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front Plant Sci 8. https:\/\/doi.org\/10.3389\/fpls.2017.01741","DOI":"10.3389\/fpls.2017.01741"},{"key":"18392_CR96","doi-asserted-by":"publisher","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 31:299\u2013315. https:\/\/doi.org\/10.1080\/08839514.2017.1315516","journal-title":"Appl Artif Intell"},{"key":"18392_CR97","doi-asserted-by":"publisher","unstructured":"DeChant C, Wiesner-Hanks T, Chen S et al (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology\u00ae 107:1426\u20131432. https:\/\/doi.org\/10.1094\/PHYTO-11-16-0417-R","DOI":"10.1094\/PHYTO-11-16-0417-R"},{"key":"18392_CR98","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"},{"key":"18392_CR99","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","volume":"180","author":"JG Arnal Barbedo","year":"2019","unstructured":"Arnal Barbedo JG (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96\u2013107. https:\/\/doi.org\/10.1016\/j.biosystemseng.2019.02.002","journal-title":"Biosyst Eng"},{"key":"18392_CR100","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-319-90403-0_6","volume-title":"Human and machine learning: Visible, explainable, trustworthy and transparent","author":"M Brahimi","year":"2018","unstructured":"Brahimi M, Arsenovic M, Laraba S et al (2018) Deep learning for plant diseases: detection and saliency map visualisation. In: Zhou J, Chen F (eds) Human and machine learning: Visible, explainable, trustworthy and transparent. Springer International Publishing, Cham, pp 93\u2013117"},{"key":"18392_CR101","doi-asserted-by":"publisher","unstructured":"Ozguven MM, Adem K (2019) Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Stat Mech Applic 535. https:\/\/doi.org\/10.1016\/j.physa.2019.122537122537","DOI":"10.1016\/j.physa.2019.122537122537"},{"key":"18392_CR102","doi-asserted-by":"publisher","unstructured":"Elhassouny A, Smarandache F (2019) Smart mobile application to recognize tomato leaf diseases using convolutional neural networks. In: 2019 International Conference of Computer Science and Renewable Energies (ICCSRE). pp 1\u20134. https:\/\/doi.org\/10.1109\/ICCSRE.2019.8807737","DOI":"10.1109\/ICCSRE.2019.8807737"},{"key":"18392_CR103","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/LGRS.2019.2932385","volume":"17","author":"EC Tetila","year":"2020","unstructured":"Tetila EC, Machado BB, Menezes GK et al (2020) Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks. IEEE Geosci Remote Sens Lett 17:903\u2013907. https:\/\/doi.org\/10.1109\/LGRS.2019.2932385","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"18392_CR104","doi-asserted-by":"publisher","first-page":"939","DOI":"10.3390\/sym11070939","volume":"11","author":"M Arsenovic","year":"2019","unstructured":"Arsenovic M, Karanovic M, Sladojevic S et al (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11:939. https:\/\/doi.org\/10.3390\/sym11070939","journal-title":"Symmetry"},{"key":"18392_CR105","doi-asserted-by":"publisher","unstructured":"Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci 9. https:\/\/doi.org\/10.3389\/fpls.2018.01162","DOI":"10.3389\/fpls.2018.01162"},{"key":"18392_CR106","doi-asserted-by":"publisher","unstructured":"Bierman A, LaPlumm T, Cadle-Davidson L et al (2019) A high-throughput phenotyping system using machine vision to quantify severity of grapevine powdery mildew. Plant Phenomics 2019. https:\/\/doi.org\/10.34133\/2019\/9209727","DOI":"10.34133\/2019\/9209727"},{"key":"18392_CR107","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 et al (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":"18392_CR108","doi-asserted-by":"publisher","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 Applic (IJACSA) 9. https:\/\/doi.org\/10.14569\/IJACSA.2018.090109","DOI":"10.14569\/IJACSA.2018.090109"},{"key":"18392_CR109","doi-asserted-by":"publisher","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","volume":"6","author":"X Zhang","year":"2018","unstructured":"Zhang X, Qiao Y, Meng F et al (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370\u201330377. https:\/\/doi.org\/10.1109\/ACCESS.2018.2844405","journal-title":"IEEE Access"},{"key":"18392_CR110","doi-asserted-by":"publisher","first-page":"e2917536","DOI":"10.1155\/2017\/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:e2917536. https:\/\/doi.org\/10.1155\/2017\/2917536","journal-title":"Comput Intell Neurosci"},{"key":"18392_CR111","unstructured":"Wallelign S, Polceanu M, Buche C (2018) Soybean plant disease identification using convolutional neural network. In: FLAIRS-31. Melbourne, United States, pp 146\u2013151. https:\/\/aaai.org\/papers\/146-flairs-2018-17682\/"},{"key":"18392_CR112","doi-asserted-by":"publisher","unstructured":"Durmu\u015f H, G\u00fcne\u015f EO, K\u0131rc\u0131 M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics. pp 1\u20135.\u00a0https:\/\/doi.org\/10.1109\/Agro-Geoinformatics.2017.8047016","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"key":"18392_CR113","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neucom.2017.06.023","volume":"267","author":"Y Lu","year":"2017","unstructured":"Lu Y, Yi S, Zeng N et al (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378\u2013384. https:\/\/doi.org\/10.1016\/j.neucom.2017.06.023","journal-title":"Neurocomputing"},{"key":"18392_CR114","doi-asserted-by":"publisher","unstructured":"Toda Y, Okura F (2019) How convolutional neural networks diagnose plant disease. Plant Phenomics 2019. https:\/\/doi.org\/10.34133\/2019\/9237136","DOI":"10.34133\/2019\/9237136"},{"key":"18392_CR115","doi-asserted-by":"publisher","unstructured":"\u00c7u\u011fu \u0130, \u015eener E, Erciyes \u00c7 et al (2017) Treelogy: A novel tree classifier utilizing deep and hand-crafted representations. https:\/\/doi.org\/10.48550\/arXiv.1701.08291","DOI":"10.48550\/arXiv.1701.08291"},{"key":"18392_CR116","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cogsys.2018.04.006","volume":"53","author":"S Zhang","year":"2019","unstructured":"Zhang S, Huang W, Zhang C (2019) Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn Syst Res 53:31\u201341. https:\/\/doi.org\/10.1016\/j.cogsys.2018.04.006","journal-title":"Cogn Syst Res"},{"key":"18392_CR117","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","volume":"141","author":"X Cheng","year":"2017","unstructured":"Cheng X, Zhang Y, Chen Y et al (2017) Pest identification via deep residual learning in complex background. Comput Electron Agric 141:351\u2013356. https:\/\/doi.org\/10.1016\/j.compag.2017.08.005","journal-title":"Comput Electron Agric"},{"key":"18392_CR118","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.070","volume":"133","author":"AK Rangarajan","year":"2018","unstructured":"Rangarajan AK, 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.2018.07.070","journal-title":"Procedia Comput Sci"},{"key":"18392_CR119","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.compag.2018.04.002","volume":"161","author":"A Picon","year":"2019","unstructured":"Picon A, Alvarez-Gila A, Seitz M et al (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280\u2013290. https:\/\/doi.org\/10.1016\/j.compag.2018.04.002","journal-title":"Comput Electron Agric"},{"key":"18392_CR120","doi-asserted-by":"publisher","first-page":"100353","DOI":"10.1016\/j.suscom.2019.100353","volume":"24","author":"G Hu","year":"2019","unstructured":"Hu G, Yang X, Zhang Y, Wan M (2019) Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput: Inform Syst 24:100353. https:\/\/doi.org\/10.1016\/j.suscom.2019.100353","journal-title":"Sustain Comput: Inform Syst"},{"key":"18392_CR121","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compag.2019.01.041","volume":"158","author":"A Kaya","year":"2019","unstructured":"Kaya A, Keceli AS, Catal C et al (2019) Analysis of transfer learning for deep neural network based plant classification models. Comput Electron Agric 158:20\u201329. https:\/\/doi.org\/10.1016\/j.compag.2019.01.041","journal-title":"Comput Electron Agric"},{"key":"18392_CR122","doi-asserted-by":"publisher","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","volume":"7","author":"UP Singh","year":"2019","unstructured":"Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:43721\u201343729. https:\/\/doi.org\/10.1109\/ACCESS.2019.2907383","journal-title":"IEEE Access"},{"key":"18392_CR123","doi-asserted-by":"publisher","first-page":"E2557","DOI":"10.3390\/s17112557","volume":"17","author":"K Yamamoto","year":"2017","unstructured":"Yamamoto K, Togami T, Yamaguchi N (2017) Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors (Basel) 17:E2557. https:\/\/doi.org\/10.3390\/s17112557","journal-title":"Sensors (Basel)"},{"key":"18392_CR124","doi-asserted-by":"publisher","unstructured":"Brahimi M, Mahmoudi S, Boukhalfa K, Moussaoui A (2019) Deep interpretable architecture for plant diseases classification. https:\/\/doi.org\/10.48550\/arXiv.1905.13523","DOI":"10.48550\/arXiv.1905.13523"},{"key":"18392_CR125","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.104948","author":"KC Kamal","year":"2019","unstructured":"Kamal KC, Yin Z, Mingyang Wu, Zhilu Wu (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric. https:\/\/doi.org\/10.1016\/j.compag.2019.104948","journal-title":"Comput Electron Agric"},{"key":"18392_CR126","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.compag.2019.03.012","volume":"162","author":"S Zhang","year":"2019","unstructured":"Zhang S, Zhang S, Zhang C et al (2019) Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput Electron Agric 162:422\u2013430. https:\/\/doi.org\/10.1016\/j.compag.2019.03.012","journal-title":"Comput Electron Agric"},{"key":"18392_CR127","doi-asserted-by":"publisher","first-page":"4377","DOI":"10.1038\/s41598-019-40066-y","volume":"9","author":"D Wang","year":"2019","unstructured":"Wang D, Vinson R, Holmes M et al (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. https:\/\/doi.org\/10.1038\/s41598-019-40066-y","journal-title":"Sci Rep"},{"key":"18392_CR128","doi-asserted-by":"publisher","unstructured":"Bhatt P, Sarangi S, Shivhare A, Singh D, Pappula S (2019) Identification of diseases in corn leaves using convolutional neural networks and boosting. In ICPRAM 894\u2013899.\u00a0https:\/\/doi.org\/10.5220\/0007687608940899","DOI":"10.5220\/0007687608940899"},{"key":"18392_CR129","doi-asserted-by":"publisher","first-page":"190006","DOI":"10.2135\/tppj2019.03.0006","volume":"2","author":"H Wu","year":"2019","unstructured":"Wu H, Wiesner-Hanks T, Stewart EL et al (2019) Autonomous detection of plant disease symptoms directly from aerial imagery. Plant Phenome J 2:190006. https:\/\/doi.org\/10.2135\/tppj2019.03.0006","journal-title":"Plant Phenome J"},{"key":"18392_CR130","doi-asserted-by":"publisher","first-page":"105456","DOI":"10.1016\/j.compag.2020.105456","volume":"175","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D et al (2020) An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput Electron Agric 175:105456. https:\/\/doi.org\/10.1016\/j.compag.2020.105456","journal-title":"Comput Electron Agric"},{"key":"18392_CR131","doi-asserted-by":"publisher","unstructured":"Richey B, Majumder S, Shirvaikar M, Kehtarnavaz N (2020) Real-time detection of maize crop disease via a deep learning-based smartphone app. In: Real-time image processing and deep learning 2020. SPIE pp 23\u201329.\u00a0https:\/\/doi.org\/10.1117\/12.2557317","DOI":"10.1117\/12.2557317"},{"key":"18392_CR132","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1016\/j.procs.2020.03.236","volume":"167","author":"S Mishra","year":"2020","unstructured":"Mishra S, Sachan R, Rajpal D (2020) Deep convolutional neural network based detection system for real-time Corn Plant disease recognition. Procedia Comput Sci 167:2003\u20132010. https:\/\/doi.org\/10.1016\/j.procs.2020.03.236","journal-title":"Procedia Comput Sci"},{"key":"18392_CR133","doi-asserted-by":"publisher","first-page":"012080","DOI":"10.1088\/1742-6596\/1437\/1\/012080","volume":"1437","author":"X Sun","year":"2020","unstructured":"Sun X, Wei J (2020) Identification of maize disease based on transfer learning. J Phys: Conf Ser 1437:012080. https:\/\/doi.org\/10.1088\/1742-6596\/1437\/1\/012080","journal-title":"J Phys: Conf Ser"},{"key":"18392_CR134","doi-asserted-by":"publisher","first-page":"012148","DOI":"10.1088\/1742-6596\/1693\/1\/012148","volume":"1693","author":"J Liu","year":"2020","unstructured":"Liu J, Wang M, Bao L, Li X (2020) EfficientNet based recognition of maize diseases by leaf image classification. J Phys: Conf Ser 1693:012148. https:\/\/doi.org\/10.1088\/1742-6596\/1693\/1\/012148","journal-title":"J Phys: Conf Ser"},{"key":"18392_CR135","doi-asserted-by":"publisher","unstructured":"Garg K, Bhugra S, Lall B (2021) Automatic quantification of plant disease from field image data using deep learning. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp 1964\u20131971. https:\/\/doi.org\/10.1109\/WACV48630.2021.00201","DOI":"10.1109\/WACV48630.2021.00201"},{"key":"18392_CR136","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1117\/12.2587892","volume-title":"Real-time image processing and deep learning 2021","author":"B Richey","year":"2021","unstructured":"Richey B, Shirvaikar MV (2021) Deep learning based real-time detection of northern corn leaf blight crop disease using YoloV4. In: Kehtarnavaz N, Carlsohn MF (eds) Real-time image processing and deep learning 2021. SPIE, Online Only, United States, p 5"},{"key":"18392_CR137","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/plants10010028","volume":"10","author":"A Afifi","year":"2021","unstructured":"Afifi A, Alhumam A, Abdelwahab A (2021) Convolutional neural network for automatic identification of plant diseases with limited data. Plants 10:28. https:\/\/doi.org\/10.3390\/plants10010028","journal-title":"Plants"},{"key":"18392_CR138","first-page":"151","volume-title":"Advances in neural computation, machine learning, and cognitive research II","author":"P Goncharov","year":"2019","unstructured":"Goncharov P, Ososkov G, Nechaevskiy A et al (2019) Disease detection on the plant leaves by deep learning. In: Kryzhanovsky B, Dunin-Barkowski W, Redko V, Tiumentsev Y (eds) Advances in neural computation, machine learning, and cognitive research II. Springer International Publishing, Cham, pp 151\u2013159"},{"key":"18392_CR139","first-page":"10","volume":"2","author":"B Ashqar","year":"2019","unstructured":"Ashqar B, Abu-Naser S (2019) Image-based tomato leaves diseases detection using deep learning. Int J Eng Res 2:10\u201316","journal-title":"Int J Eng Res"},{"key":"18392_CR140","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.3390\/plants9111451","volume":"9","author":"MH Saleem","year":"2020","unstructured":"Saleem MH, Khanchi S, Potgieter J, Arif KM (2020) Image-based plant disease identification by deep learning meta-architectures. Plants 9:1451. https:\/\/doi.org\/10.3390\/plants9111451","journal-title":"Plants"},{"key":"18392_CR141","doi-asserted-by":"publisher","first-page":"25763","DOI":"10.1007\/s11042-020-09244-3","volume":"79","author":"MA Khan","year":"2020","unstructured":"Khan MA, Akram T, Sharif M, Saba T (2020) Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection. Multimed Tools Appl 79:25763\u201325783. https:\/\/doi.org\/10.1007\/s11042-020-09244-3","journal-title":"Multimed Tools Appl"},{"key":"18392_CR142","doi-asserted-by":"publisher","first-page":"e2479172","DOI":"10.1155\/2020\/2479172","volume":"2020","author":"Y Guo","year":"2020","unstructured":"Guo Y, Zhang J, Yin C et al (2020) Plant disease identification based on deep learning algorithm in smart farming. Discret Dyn Nat Soc 2020:e2479172. https:\/\/doi.org\/10.1155\/2020\/2479172","journal-title":"Discret Dyn Nat Soc"},{"key":"18392_CR143","doi-asserted-by":"publisher","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":"V Vimal Adit","year":"2020","unstructured":"Vimal Adit V, Rubesh CV, Sanjay Bharathi S et al (2020) A comparison of deep learning algorithms for plant disease classification. In: Gunjan VK, Senatore S, Kumar A et al (eds) Advances in cybernetics, cognition, and machine learning for communication technologies. Springer, Singapore, pp 153\u2013161"},{"key":"18392_CR144","doi-asserted-by":"publisher","first-page":"012009","DOI":"10.1088\/1742-6596\/1845\/1\/012009","volume":"1845","author":"A Sembiring","year":"2021","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. J Phys: Conf Ser 1845:012009. https:\/\/doi.org\/10.1088\/1742-6596\/1845\/1\/012009","journal-title":"J Phys: Conf Ser"},{"key":"18392_CR145","doi-asserted-by":"publisher","DOI":"10.1007\/s10772-021-09843-x","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\/s10772-021-09843-x","journal-title":"Int J Speech Technol"},{"key":"18392_CR146","doi-asserted-by":"publisher","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"\u00dc Atila","year":"2021","unstructured":"Atila \u00dc, U\u00e7ar M, Akyol K, U\u00e7ar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Eco Inform 61:101182. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101182","journal-title":"Eco Inform"},{"key":"18392_CR147","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s11760-021-01909-2","volume":"16","author":"M Turkoglu","year":"2022","unstructured":"Turkoglu M, Yaniko\u011flu B, Hanbay D (2022) PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection. SIViP 16:301\u2013309. https:\/\/doi.org\/10.1007\/s11760-021-01909-2","journal-title":"SIViP"},{"key":"18392_CR148","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s10489-021-02452-w","volume":"52","author":"SF Syed-Ab-Rahman","year":"2022","unstructured":"Syed-Ab-Rahman SF, Hesamian MH, Prasad M (2022) Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell 52:927\u2013938. https:\/\/doi.org\/10.1007\/s10489-021-02452-w","journal-title":"Appl Intell"},{"key":"18392_CR149","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s11104-022-05407-3","volume":"477","author":"A Yadav","year":"2022","unstructured":"Yadav A, Thakur U, Saxena R et al (2022) AFD-net: Apple foliar disease multi classification using deep learning on plant pathology dataset. Plant Soil 477:595\u2013611. https:\/\/doi.org\/10.1007\/s11104-022-05407-3","journal-title":"Plant Soil"},{"key":"18392_CR150","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.inpa.2021.06.001","volume":"9","author":"D Shah","year":"2022","unstructured":"Shah D, Trivedi V, Sheth V et al (2022) ResTS: Residual deep interpretable architecture for plant disease detection. Inf Process Agric 9:212\u2013223. https:\/\/doi.org\/10.1016\/j.inpa.2021.06.001","journal-title":"Inf Process Agric"},{"key":"18392_CR151","doi-asserted-by":"publisher","unstructured":"Dananjayan S, Tang Y, Zhuang J et al (2022) Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Comput Electron Agric 193. https:\/\/doi.org\/10.1016\/j.compag.2021.106658","DOI":"10.1016\/j.compag.2021.106658"},{"key":"18392_CR152","doi-asserted-by":"publisher","first-page":"106703","DOI":"10.1016\/j.compag.2022.106703","volume":"193","author":"X Zhao","year":"2022","unstructured":"Zhao X, Li K, Li Y et al (2022) Identification method of vegetable diseases based on transfer learning and attention mechanism. Comput Electron Agric 193:106703. https:\/\/doi.org\/10.1016\/j.compag.2022.106703","journal-title":"Comput Electron Agric"},{"key":"18392_CR153","doi-asserted-by":"publisher","first-page":"106641","DOI":"10.1016\/j.compag.2021.106641","volume":"193","author":"G Li","year":"2022","unstructured":"Li G, Suo R, Zhao G et al (2022) Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination. Comput Electron Agric 193:106641. https:\/\/doi.org\/10.1016\/j.compag.2021.106641","journal-title":"Comput Electron Agric"},{"key":"18392_CR154","doi-asserted-by":"publisher","first-page":"106718","DOI":"10.1016\/j.compag.2022.106718","volume":"193","author":"M Ji","year":"2022","unstructured":"Ji M, Wu Z (2022) Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic. Comput Electron Agric 193:106718. https:\/\/doi.org\/10.1016\/j.compag.2022.106718","journal-title":"Comput Electron Agric"},{"key":"18392_CR155","doi-asserted-by":"publisher","first-page":"495","DOI":"10.3390\/electronics11030495","volume":"11","author":"P Dhiman","year":"2022","unstructured":"Dhiman P, Kukreja V, Manoharan P et al (2022) A novel deep learning model for detection of severity level of the disease in citrus fruits. Electronics 11:495. https:\/\/doi.org\/10.3390\/electronics11030495","journal-title":"Electronics"},{"key":"18392_CR156","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s41348-021-00465-8","volume":"129","author":"S Vallabhajosyula","year":"2022","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK (2022) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129:545\u2013558. https:\/\/doi.org\/10.1007\/s41348-021-00465-8","journal-title":"J Plant Dis Prot"},{"key":"18392_CR157","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s40747-021-00536-1","volume":"8","author":"W Albattah","year":"2022","unstructured":"Albattah W, Nawaz M, Javed A et al (2022) A novel deep learning method for detection and classification of plant diseases. Complex Intell Syst 8:507\u2013524. https:\/\/doi.org\/10.1007\/s40747-021-00536-1","journal-title":"Complex Intell Syst"},{"key":"18392_CR158","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3390\/agronomy12020365","volume":"12","author":"Z Chen","year":"2022","unstructured":"Chen Z, Wu R, Lin Y et al (2022) Plant disease recognition model based on improved YOLOv5. Agronomy 12:365. https:\/\/doi.org\/10.3390\/agronomy12020365","journal-title":"Agronomy"},{"key":"18392_CR159","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.3390\/s22072696","volume":"22","author":"RG Dawod","year":"2022","unstructured":"Dawod RG, Dobre C (2022) Upper and lower leaf side detection with machine learning methods. Sensors 22:2696. https:\/\/doi.org\/10.3390\/s22072696","journal-title":"Sensors"},{"key":"18392_CR160","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-04334-6","author":"J Chen","year":"2022","unstructured":"Chen J, Zeb A, Nanehkaran YA, Zhang D (2022) Stacking ensemble model of deep learning for plant disease recognition. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-022-04334-6","journal-title":"J Ambient Intell Human Comput"},{"key":"18392_CR161","doi-asserted-by":"publisher","first-page":"468","DOI":"10.3390\/plants8110468","volume":"8","author":"MH Saleem","year":"2019","unstructured":"Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8:468. https:\/\/doi.org\/10.3390\/plants8110468","journal-title":"Plants"},{"issue":"6","key":"18392_CR162","first-page":"82","volume":"41","author":"X-P Fan","year":"2020","unstructured":"Fan X-P, Zhou J-P, Xu Y (2020) Recognition of field maize leaf diseases based on improved regional convolutional neural network. J South China Agric Univ 41(6):82\u201391","journal-title":"J South China Agric Univ"},{"key":"18392_CR163","doi-asserted-by":"publisher","unstructured":"Junde Chen, Jinxiu Chen, Defu Zhang et al (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393-. https:\/\/doi.org\/10.1016\/j.compag.2020.105393","DOI":"10.1016\/j.compag.2020.105393"},{"key":"18392_CR164","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s13007-020-00624-2","volume":"16","author":"J Liu","year":"2020","unstructured":"Liu J, Wang X (2020) Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16:83. https:\/\/doi.org\/10.1186\/s13007-020-00624-2","journal-title":"Plant Methods"},{"key":"18392_CR165","doi-asserted-by":"publisher","unstructured":"Wang X, Liu J (2021) Tomato anomalies detection in greenhouse scenarios based on YOLO-Dense. Front Plant Sci 12. https:\/\/doi.org\/10.3389\/fpls.2021.634103","DOI":"10.3389\/fpls.2021.634103"},{"key":"18392_CR166","doi-asserted-by":"publisher","unstructured":"Abadi M, Barham P, Chen J, et al (2016) TensorFlow: A system for large-scale machine learning.\u00a0. arXiv: Distributed, Parallel, and Cluster Computing. https:\/\/doi.org\/10.48550\/arXiv.1605.08695","DOI":"10.48550\/arXiv.1605.08695"},{"key":"18392_CR167","unstructured":"Paszke A, Gross S, Chintala S et al (2017) Automatic differentiation in PyTorch. https:\/\/api.semanticscholar.org\/CorpusID:4002767"},{"key":"18392_CR168","doi-asserted-by":"publisher","unstructured":"Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: Convolutional architecture for fast feature embedding.\u00a0Computer Science : computer vision and pattern recognition. https:\/\/doi.org\/10.48550\/arXiv.1408.5093","DOI":"10.48550\/arXiv.1408.5093"},{"key":"18392_CR169","doi-asserted-by":"publisher","unstructured":"Chen T, Li M, Li Y et al (2015) MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems.\u00a0Computer Science: distributed, parallel, and cluster computing.\nhttps:\/\/doi.org\/10.48550\/arXiv.1512.01274","DOI":"10.48550\/arXiv.1512.01274"},{"key":"18392_CR170","doi-asserted-by":"publisher","first-page":"60503","DOI":"10.1117\/1.JBO.22.6.060503","volume":"22","author":"M Halicek","year":"2017","unstructured":"Halicek M, Lu G, Little JV et al (2017) Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 22:60503. https:\/\/doi.org\/10.1117\/1.JBO.22.6.060503","journal-title":"J Biomed Opt"},{"key":"18392_CR171","doi-asserted-by":"crossref","unstructured":"Singh P, Pandey PC, Petropoulos GP et al (2020) 8 - Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends. In: Pandey PC, Srivastava PK, Balzter H et al (eds) Hyperspectral remote sensing. Elsevier, pp 121\u2013146","DOI":"10.1016\/B978-0-08-102894-0.00009-7"},{"key":"18392_CR172","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.3390\/rs12193188","volume":"12","author":"N Zhang","year":"2020","unstructured":"Zhang N, Yang G, Pan Y et al (2020) A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens 12:3188. https:\/\/doi.org\/10.3390\/rs12193188","journal-title":"Remote Sens"},{"key":"18392_CR173","doi-asserted-by":"publisher","first-page":"105998","DOI":"10.1016\/j.knosys.2020.105998","volume":"200","author":"S Zhang","year":"2020","unstructured":"Zhang S, Zhang C, Wang X (2020) Plant species recognition based on global\u2013local maximum margin discriminant projection. Knowl-Based Syst 200:105998. https:\/\/doi.org\/10.1016\/j.knosys.2020.105998","journal-title":"Knowl-Based Syst"},{"key":"18392_CR174","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.3390\/rs13193841","volume":"13","author":"K Neupane","year":"2021","unstructured":"Neupane K, Baysal-Gurel F (2021) Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sens 13:3841. https:\/\/doi.org\/10.3390\/rs13193841","journal-title":"Remote Sens"},{"key":"18392_CR175","doi-asserted-by":"publisher","first-page":"3830","DOI":"10.3390\/s21113830","volume":"21","author":"A Almadhor","year":"2021","unstructured":"Almadhor A, Rauf HT, Lali MIU et al (2021) AI-driven framework for recognition of guava plant diseases through machine learning from DSLR camera sensor based high resolution imagery. Sensors 21:3830. https:\/\/doi.org\/10.3390\/s21113830","journal-title":"Sensors"},{"key":"18392_CR176","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.compag.2018.04.023","volume":"150","author":"M Sharif","year":"2018","unstructured":"Sharif M, Khan MA, Iqbal Z et al (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220\u2013234. https:\/\/doi.org\/10.1016\/j.compag.2018.04.023","journal-title":"Comput Electron Agric"},{"key":"18392_CR177","doi-asserted-by":"publisher","first-page":"16347","DOI":"10.1007\/s00500-020-04946-0","volume":"24","author":"MP Vaishnnave","year":"2020","unstructured":"Vaishnnave MP, Suganya Devi K, Ganeshkumar P (2020) Automatic method for classification of groundnut diseases using deep convolutional neural network. Soft Comput 24:16347\u201316360. https:\/\/doi.org\/10.1007\/s00500-020-04946-0","journal-title":"Soft Comput"},{"key":"18392_CR178","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s11760-020-01780-7","volume":"15","author":"Y Kurmi","year":"2021","unstructured":"Kurmi Y, Gangwar S, Agrawal D et al (2021) Leaf image analysis-based crop diseases classification. SIViP 15:589\u2013597. https:\/\/doi.org\/10.1007\/s11760-020-01780-7","journal-title":"SIViP"},{"key":"18392_CR179","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.compag.2019.105162","journal-title":"Comput Electron Agric"},{"key":"18392_CR180","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1186\/s13007-017-0233-z","journal-title":"Plant Methods"},{"key":"18392_CR181","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","volume":"100","author":"A-K Mahlein","year":"2016","unstructured":"Mahlein A-K (2016) Plant disease detection by imaging sensors \u2013 parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241\u2013251. https:\/\/doi.org\/10.1094\/PDIS-03-15-0340-FE","journal-title":"Plant Dis"},{"key":"18392_CR182","unstructured":"Hughes DP, Salathe M (2016) An open access repository of images on plant health to enable the development of mobile disease diagnostics"},{"key":"18392_CR183","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1109\/TLA.2018.8444395","volume":"16","author":"J Garcia Arnal Barbedo","year":"2018","unstructured":"Garcia Arnal Barbedo J, Vieira Koenigkan L, Almeida Halfeld-Vieira B et al (2018) Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Lat Am Trans 16:1749\u20131757. https:\/\/doi.org\/10.1109\/TLA.2018.8444395","journal-title":"IEEE Lat Am Trans"},{"key":"18392_CR184","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","volume":"153","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018) 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. https:\/\/doi.org\/10.1016\/j.compag.2018.08.013","journal-title":"Comput Electron Agric"},{"key":"18392_CR185","doi-asserted-by":"publisher","unstructured":"Liu B, Ding Z, Tian L et al (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11. https:\/\/doi.org\/10.3389\/fpls.2020.01082","DOI":"10.3389\/fpls.2020.01082"},{"key":"18392_CR186","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s41348-020-00403-0","volume":"128","author":"R Thangaraj","year":"2021","unstructured":"Thangaraj R, Anandamurugan S, Kaliappan VK (2021) Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J Plant Dis Prot 128:73\u201386. https:\/\/doi.org\/10.1007\/s41348-020-00403-0","journal-title":"J Plant Dis Prot"},{"key":"18392_CR187","doi-asserted-by":"publisher","unstructured":"Thangaraj R, Anandamurugan S, Pandiyan P, Kaliappan VK (2022)\u00a0Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion. J Plant Dis Prot\u00a0129:469\u2013488. https:\/\/doi.org\/10.1007\/s41348-021-00500-8","DOI":"10.1007\/s41348-021-00500-8"},{"key":"18392_CR188","doi-asserted-by":"publisher","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric\u00a0145:311\u2013318. https:\/\/doi.org\/10.1016\/j.compag.2018.01.009","DOI":"10.1016\/j.compag.2018.01.009"},{"key":"18392_CR189","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":"18392_CR190","doi-asserted-by":"publisher","first-page":"898","DOI":"10.3389\/fpls.2020.00898","volume":"11","author":"J Liu","year":"2020","unstructured":"Liu J, Wang X (2020) Tomato diseases and pests detection based on improved yolo V3 convolutional neural network. Front Plant Sci 11:898. https:\/\/doi.org\/10.3389\/fpls.2020.00898","journal-title":"Front Plant Sci"},{"key":"18392_CR191","doi-asserted-by":"publisher","unstructured":"Kassim MRM (2020) IoT applications in smart agriculture: Issues and challenges. In: 2020 IEEE Conference on Open Systems (ICOS). pp 19\u201324. https:\/\/doi.org\/10.1109\/ICOS50156.2020.9293672","DOI":"10.1109\/ICOS50156.2020.9293672"},{"key":"18392_CR192","doi-asserted-by":"publisher","unstructured":"Morchid A, El Alami R, Raezah AA, Sabbar Y (2023) Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges. Ain Shams Eng J 102509. https:\/\/doi.org\/10.1016\/j.asej.2023.102509","DOI":"10.1016\/j.asej.2023.102509"},{"key":"18392_CR193","doi-asserted-by":"publisher","first-page":"100048","DOI":"10.1016\/j.array.2020.100048","volume":"8","author":"R de Araujo","year":"2020","unstructured":"de Araujo R, Zanella A, da Silva E, Pessoa Albini LC (2020) Security challenges to smart agriculture: Current state, key issues, and future directions. Array 8:100048. https:\/\/doi.org\/10.1016\/j.array.2020.100048","journal-title":"Array"},{"key":"18392_CR194","doi-asserted-by":"publisher","first-page":"107037","DOI":"10.1016\/j.comnet.2019.107037","volume":"168","author":"D Glaroudis","year":"2020","unstructured":"Glaroudis D, Iossifides A, Chatzimisios P (2020) Survey, comparison and research challenges of IoT application protocols for smart farming. Comput Netw 168:107037. https:\/\/doi.org\/10.1016\/j.comnet.2019.107037","journal-title":"Comput Netw"},{"key":"18392_CR195","doi-asserted-by":"publisher","first-page":"106352","DOI":"10.1016\/j.compag.2021.106352","volume":"189","author":"W Tao","year":"2021","unstructured":"Tao W, Zhao L, Wang G, Liang R (2021) Review of the internet of things communication technologies in smart agriculture and challenges. Comput Electron Agric 189:106352. https:\/\/doi.org\/10.1016\/j.compag.2021.106352","journal-title":"Comput Electron Agric"},{"key":"18392_CR196","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.future.2021.08.006","volume":"126","author":"BB Sinha","year":"2022","unstructured":"Sinha BB, Dhanalakshmi R (2022) Recent advancements and challenges of internet of things in smart agriculture: A survey. Futur Gener Comput Syst 126:169\u2013184. https:\/\/doi.org\/10.1016\/j.future.2021.08.006","journal-title":"Futur Gener Comput Syst"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18392-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18392-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18392-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T17:11:37Z","timestamp":1722359497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18392-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":196,"journal-issue":{"issue":"28","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["18392"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18392-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"14 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":4,"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 competing financial interests or personal relationships that could have appeared to influence the work reported in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}