{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:42:51Z","timestamp":1766486571009,"version":"3.41.2"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key R & D and Promotion Projects in Henan Province","award":["232102110265"],"award-info":[{"award-number":["232102110265"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20288-7","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:02:23Z","timestamp":1727679743000},"page":"28559-28581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on hotspots and frontiers of agricultural pests and diseases image recognition technology based on bibliometrics"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9843-2877","authenticated-orcid":false,"given":"Hongtao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"20288_CR1","unstructured":"Fu Z, Qi L (1998) Over use of pesticide and approaches to reduce pesticide dosage. Trans Chin Soc Agric Eng (Transactions of the C-SAE) 14(2):7\u201312. http:\/\/www.tcsae.org\/en\/article\/id\/19980202"},{"key":"20288_CR2","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1146\/annurev.en.25.010180.001251","volume":"25","author":"RL Metcalf","year":"1980","unstructured":"Metcalf RL (1980) Changing role of insecticides in crop protection. Annu Rev Entomol 25:219\u2013256. https:\/\/doi.org\/10.1146\/annurev.en.25.010180.001251","journal-title":"Annu Rev Entomol"},{"issue":"03","key":"20288_CR3","doi-asserted-by":"publisher","first-page":"842","DOI":"10.7679\/j.issn.2095-1353.2014.102","volume":"51","author":"R Wang","year":"2014","unstructured":"Wang R, Lu M, Han L, Yu F, Chen F (2014) Methods and technologies for surveying and sampling the rice planthoppers, nilaparvata lugens, sogatella furcifera and laodelphax striatellus. Chin J Appl Entomol 51(03):842\u2013847. https:\/\/doi.org\/10.7679\/j.issn.2095-1353.2014.102","journal-title":"Chin J Appl Entomol"},{"issue":"9","key":"20288_CR4","doi-asserted-by":"publisher","first-page":"3253","DOI":"10.1002\/ps.5882","volume":"76","author":"G Thoming","year":"2020","unstructured":"Thoming G, Solhaug KA, Norli HR (2020) Kairomone - assisted trap cropping for protecting spring oilseed rape (brassica napus) from pollen beetles (coleoptera: nitidulidae). Pest Manag Sci 76(9):3253\u20133263. https:\/\/doi.org\/10.1002\/ps.5882","journal-title":"Pest Manag Sci"},{"issue":"3","key":"20288_CR5","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/s11119-016-9494-1","volume":"96","author":"RM Giblin-Davis","year":"2013","unstructured":"Giblin-Davis RM, Roda AL (2013) Real time internet invasive pest identification training: a case study with rhynchophorus weevils. Fla Entomol 96(3):741\u2013745. https:\/\/doi.org\/10.1007\/s11119-016-9494-1","journal-title":"Fla Entomol"},{"key":"20288_CR6","doi-asserted-by":"publisher","first-page":"403","DOI":"10.14411\/eje.2016.052","volume":"113","author":"LCP Silveira","year":"2016","unstructured":"Silveira LCP, Haro M (2016) Fast slide preparation for thrips (Thysanoptera) routine identifications. Eur J Entomol 113:403\u2013408. https:\/\/doi.org\/10.14411\/eje.2016.052","journal-title":"Eur J Entomol"},{"issue":"01","key":"20288_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.16452\/j.cnki.sdkjsk.20200106.002","volume":"22","author":"D Yang","year":"2020","unstructured":"Yang D, Wu P, Chen H (2020) Bibliometric analysis of domestic computable general equilibrium studies based on CiteSpace. J Shandong Univ Sci Technol (Social Science Edition) 22(01):111\u2013120. https:\/\/doi.org\/10.16452\/j.cnki.sdkjsk.20200106.002","journal-title":"J Shandong Univ Sci Technol (Social Science Edition)"},{"key":"20288_CR8","unstructured":"Peng Z, Wu Q, Chen H, Zheng Y, Wang S (2021) Review of research on machine vision defect detection based on literature measurement. Comput Eng Appl 57(04): 28\u201334. https:\/\/link.cnki.net\/urlid\/11.2127.TP.20210107.1522.010"},{"issue":"4","key":"20288_CR9","doi-asserted-by":"publisher","first-page":"9229","DOI":"10.15666\/aeer\/1704_92299245","volume":"17","author":"YR Wu","year":"2019","unstructured":"Wu YR, Li JH (2019) Multi-feature sparse constrain model for crop disease Recognition. Appl Ecol Environ Res 17(4):9229\u20139245. https:\/\/doi.org\/10.15666\/aeer\/1704_92299245","journal-title":"Appl Ecol Environ Res"},{"key":"20288_CR10","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1007\/s41348-022-00608-5","volume":"129","author":"AB Djimeli-Tsajio","year":"2022","unstructured":"Djimeli-Tsajio AB, Thierry N, Jean-Pierre LT, Kapche TF, Nagabhushan P (2022) Improved detection and identification approach in tomato leaf disease using transformation and combination of transfer learning features. J Plant Dis Protect 129:665\u2013674. https:\/\/doi.org\/10.1007\/s41348-022-00608-5","journal-title":"J Plant Dis Protect"},{"issue":"8","key":"20288_CR11","doi-asserted-by":"publisher","first-page":"6815","DOI":"10.3390\/su15086815","volume":"15","author":"S Khalid","year":"2023","unstructured":"Khalid S, Oqaibi HM, Aqib M, Hafeez Y (2023) Small pests detection in field crops using deep learning object detection. Sustainability 15(8):6815. https:\/\/doi.org\/10.3390\/su15086815","journal-title":"Sustainability"},{"key":"20288_CR12","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.asoc.2015.08.027","volume":"37","author":"M Perez-Ortiz","year":"2015","unstructured":"Perez-Ortiz M, Pena JM, Gutierrez PA, Torres-Sanchez J, Hervas-Martinez C, Lopez-Granados F (2015) A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl Soft Comput 37:533\u2013544. https:\/\/doi.org\/10.1016\/j.asoc.2015.08.027","journal-title":"Appl Soft Comput"},{"key":"20288_CR13","doi-asserted-by":"publisher","unstructured":"Jia Z, Ou C, Sun S, Wang J, Liu J, Li M, Jia S, Mao P (2023) A novel approach using multispectral imaging for rapid development of seed pellet formulations to mitigate drought stress in alfalfa. Comput Electron Agric 212. https:\/\/doi.org\/10.1016\/j.compag.2023.108136","DOI":"10.1016\/j.compag.2023.108136"},{"issue":"2","key":"20288_CR14","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-cam: visual explanations from deep networks via gradient-based localization. Int J Comput Vision 128(2):336\u2013359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int J Comput Vision"},{"key":"20288_CR15","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.biosystemseng.2020.03.020","volume":"194","author":"RR Chowdhury","year":"2020","unstructured":"Chowdhury RR, Arko PS, Ali ME, Mohammad AIK, Sajid HA, Farzana N, Abu W (2020) Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst Eng 194:112\u2013120. https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.03.020","journal-title":"Biosyst Eng"},{"key":"20288_CR16","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","volume":"137","author":"MA Ebrahimi","year":"2017","unstructured":"Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on svm classification method. Comput Electron Agric 137:52\u201358. https:\/\/doi.org\/10.1016\/j.compag.2017.03.016","journal-title":"Comput Electron Agric"},{"issue":"07","key":"20288_CR17","doi-asserted-by":"publisher","first-page":"98","DOI":"10.3969\/j.issn.1674-1021.2022.07.024","volume":"42","author":"Y Liu","year":"2022","unstructured":"Liu Y, Hu Q (2022) Research status and prospect of local environmental protection ver tical management system reform in China\u2014Knowledge graph analysis based on CiteSpace. Environ Prot Circ Econ 42(07):98\u2013101. https:\/\/doi.org\/10.3969\/j.issn.1674-1021.2022.07.024","journal-title":"Environ Prot Circ Econ"},{"key":"20288_CR18","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, Liu Y, Zhang Y (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":"20288_CR19","doi-asserted-by":"publisher","unstructured":"Sethy DK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric 175. https:\/\/doi.org\/10.1016\/j.compag.2020.105527","DOI":"10.1016\/j.compag.2020.105527"},{"key":"20288_CR20","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1016\/j.ecoinf.2021.101515","volume":"67","author":"L Nanni","year":"2021","unstructured":"Nanni L, Manfe A, Maguolo G, Lumini A, Brahnam S (2021) High performing ensemble of convolutional neural networks for insect pest image detection. Eco Inform 67:1574\u20139541. https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101515","journal-title":"Eco Inform"},{"key":"20288_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s11119-022-09927-x","volume":"24","author":"I Bhakta","year":"2023","unstructured":"Bhakta I, Phadikar S, Majumder K, Mukherjee H, Sau A (2023) A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agric 24:23\u201339. https:\/\/doi.org\/10.1007\/s11119-022-09927-x","journal-title":"Precision Agric"},{"issue":"6","key":"20288_CR22","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.18280\/ts.400525","volume":"40","author":"B Thokala","year":"2023","unstructured":"Thokala B, Doraikannan S (2023) Detection and classification of plant stress using hybrid deep convolution neural networks: A multi-scale vision transformer approach. Traitement du Signal 40(6):2635\u20132647. https:\/\/doi.org\/10.18280\/ts.400525","journal-title":"Traitement du Signal"},{"key":"20288_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s11119-016-9494-1","volume":"19","author":"I Garc\u00eda-Santill\u00e1n","year":"2018","unstructured":"Garc\u00eda-Santill\u00e1n I, Guerrero JM, Montalvo M, Pajares G (2018) Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agric 19:18\u201341. https:\/\/doi.org\/10.1007\/s11119-016-9494-1","journal-title":"Precision Agric"},{"key":"20288_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.compag.2016.02.002","volume":"123","author":"G Jiang","year":"2016","unstructured":"Jiang G, Wang X, Wang Z, Liu H (2016) Wheat rows detection at the early growth stage based on hough transform and vanishing point. Comput Electron Agric 123:211\u2013223. https:\/\/doi.org\/10.1016\/j.compag.2016.02.002","journal-title":"Comput Electron Agric"},{"key":"20288_CR25","doi-asserted-by":"publisher","unstructured":"Liu D, Wang Y, Chen Y, Matson ET (2019) Application of color filter adjustment and k-means clustering method in lane detection for self-driving cars. 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy, pp:153\u2013158. https:\/\/doi.org\/10.1109\/IRC.2019.00030","DOI":"10.1109\/IRC.2019.00030"},{"issue":"01","key":"20288_CR26","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1142\/S1793351X20500038","volume":"14","author":"D Liu","year":"2020","unstructured":"Liu D, Wang Y, Chen Y, Matson ET (2020) Accurate lane detection for self-driving cars: An approach based on color filter adjustment and k-means clustering filter. Int J Semantic Comput 14(01):153\u2013168. https:\/\/doi.org\/10.1142\/S1793351X20500038","journal-title":"Int J Semantic Comput"},{"key":"20288_CR27","doi-asserted-by":"publisher","first-page":"32349","DOI":"10.1007\/s11042-023-14751-0","volume":"82","author":"BS Shedthi","year":"2023","unstructured":"Shedthi BS, Siddappa M, Shetty S, Shetty V, Suresh R (2023) Detection and classification of diseased plant leaf images using hybrid algorithm. Multimed Tools Appl 82:32349\u201332372. https:\/\/doi.org\/10.1007\/s11042-023-14751-0","journal-title":"Multimed Tools Appl"},{"key":"20288_CR28","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","volume":"143","author":"DSF Alessandro","year":"2017","unstructured":"Alessandro DSF, Matte FD, Gercina GDS, Pistori H, Theophilo FM (2017) Weed detection in soybean crops using convnets. Comput Electron Agric 143:314\u2013324. https:\/\/doi.org\/10.1016\/j.compag.2017.10.027","journal-title":"Comput Electron Agric"},{"key":"20288_CR29","doi-asserted-by":"publisher","first-page":"628","DOI":"10.48550\/arXiv.2112.07819","volume":"74","author":"ASMM Hasan","year":"2021","unstructured":"Hasan ASMM, Sohel F, Diepeveen D, Laga H, Jones MGK (2021) Weed recognition using deep learning techniques on class-imbalanced imagery. Crop Pasture Sci 74:628\u2013644. https:\/\/doi.org\/10.48550\/arXiv.2112.07819","journal-title":"Crop Pasture Sci"},{"key":"20288_CR30","doi-asserted-by":"publisher","unstructured":"Hu Y, Meng A, Wu Y, Zou L, Jin Z, Xu T (2023) Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images. Front Plant Sci 14. https:\/\/doi.org\/10.3389\/fpls.2023.1124939","DOI":"10.3389\/fpls.2023.1124939"},{"key":"20288_CR31","doi-asserted-by":"publisher","unstructured":"Amorim WP, Tetila EC, Pistori H, Papa JP (2019) Semi-supervised learning with convolutional neural networks for uav images automatic recognition. Comput Electron Agric 164. https:\/\/doi.org\/10.1016\/j.compag.2019.104932","DOI":"10.1016\/j.compag.2019.104932"},{"key":"20288_CR32","doi-asserted-by":"publisher","first-page":"0168","DOI":"10.1111\/ppa.13322","volume":"173","author":"J Chen","year":"2020","unstructured":"Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:0168\u20131699. https:\/\/doi.org\/10.1111\/ppa.13322","journal-title":"Comput Electron Agric"},{"key":"20288_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2021.3069646","volume":"99","author":"L Li","year":"2021","unstructured":"Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning\u2014a review. IEEE Access 99:1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2021.3069646","journal-title":"IEEE Access"},{"issue":"6","key":"20288_CR34","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1071\/CP21710","volume":"74","author":"A Amrani","year":"2020","unstructured":"Amrani A, Sohel F, Diepeveen D, Murray D, Jones MG (2020) Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network. Crop Pasture Sci 74(6):615\u2013627. https:\/\/doi.org\/10.1071\/CP21710","journal-title":"Crop Pasture Sci"},{"issue":"3","key":"20288_CR35","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1111\/ppa.13322","volume":"70","author":"J Chen","year":"2020","unstructured":"Chen J, Wang W, Zhang D, Zeb A, Nanehkaran YA (2020) Attention embedded lightweight network for maize disease recognition. Plant Pathol 70(3):630\u2013642. https:\/\/doi.org\/10.1111\/ppa.13322","journal-title":"Plant Pathol"},{"key":"20288_CR36","doi-asserted-by":"publisher","unstructured":"Marino S (2023) Understanding the spatio-temporal behavior of crop yield, yield components and weed pressure using time series Sentinel-2-data in an organic farming system. Eur J Agron 145. https:\/\/doi.org\/10.1016\/j.eja.2023.126785","DOI":"10.1016\/j.eja.2023.126785"},{"key":"20288_CR37","doi-asserted-by":"publisher","unstructured":"Falco N, Wainwright HM, Dafflon B, Ulrich C, Soom F, Peterson JE, Brown JB, Schaettle KB, Williamson M, Cothren JD, Ham, RG, McEntire JA, Hubbard SS (2021) Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery. Sci Rep 11(1). https:\/\/doi.org\/10.1038\/s41598-021-86480-z","DOI":"10.1038\/s41598-021-86480-z"},{"key":"20288_CR38","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, Salathe 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"},{"issue":"5","key":"20288_CR39","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1109\/TCYB.2022.3169773","volume":"53","author":"M Zhang","year":"2023","unstructured":"Zhang M, Li W, Zhang Y, Tao R, Du Q (2023) Hyperspectral and LiDAR data classification based on structural optimization transmission. IEEE Trans Cybern 53(5):3153\u20133164. https:\/\/doi.org\/10.1109\/TCYB.2022.3169773","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"20288_CR40","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s11119-018-09623-9","volume":"20","author":"DB Marin","year":"2019","unstructured":"Marin DB, Alves MD, Pozza EA, Belan LL, Freitas MLD (2019) Multispectral radiometric monitoring of bacterial blight of coffee. Precision Agric 20(5):959\u2013982. https:\/\/doi.org\/10.1007\/s11119-018-09623-9","journal-title":"Precision Agric"},{"key":"20288_CR41","doi-asserted-by":"publisher","first-page":"103755","DOI":"10.1016\/j.jvcir.2023.103755","volume":"91","author":"X Hu","year":"2023","unstructured":"Hu X, Zhu S, Peng T (2023) Hierarchical attention vision transformer for fine-grained visual classification. J Vis Commun Image Represent 91:103755. https:\/\/doi.org\/10.1016\/j.jvcir.2023.103755","journal-title":"J Vis Commun Image Represent"},{"key":"20288_CR42","doi-asserted-by":"publisher","unstructured":"Wang J, Bretz M, Dewan MA, Delavar MA (2022) Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Sci Total Environ 822. https:\/\/doi.org\/10.1016\/j.scitotenv.2022.153559","DOI":"10.1016\/j.scitotenv.2022.153559"},{"key":"20288_CR43","doi-asserted-by":"publisher","unstructured":"Wang X, Hou M, Shi S, Hu Z, Yin C, Xu L (2023) Winter wheat extraction using time-series sentinel-2 data based on enhanced TWDTW in Henan Province, China. Sustainability 15(2). https:\/\/doi.org\/10.3390\/su15021490","DOI":"10.3390\/su15021490"},{"issue":"3","key":"20288_CR44","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1080\/10095020.2022.2100287","volume":"26","author":"A Tariq","year":"2023","unstructured":"Tariq A, Yan J, Gagnon AS, Khan MR, Mumtaz F (2023) Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spat Inf Sci 26(3):302\u2013320. https:\/\/doi.org\/10.1080\/10095020.2022.2100287","journal-title":"Geo-Spat Inf Sci"},{"issue":"3","key":"20288_CR45","doi-asserted-by":"publisher","first-page":"e09071","DOI":"10.1016\/j.heliyon.2022.e09071","volume":"8","author":"HG Kuma","year":"2022","unstructured":"Kuma HG, Feyessa FF, Demissie TA (2022) Land-use\/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants. Heliyon 8(3):e09071. https:\/\/doi.org\/10.1016\/j.heliyon.2022.e09071","journal-title":"Heliyon"},{"key":"20288_CR46","doi-asserted-by":"publisher","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. Precision Agric 21:955\u2013978. https:\/\/doi.org\/10.1007\/s11119-019-09703-4","journal-title":"Precision Agric"},{"issue":"5","key":"20288_CR47","doi-asserted-by":"publisher","first-page":"387","DOI":"10.3390\/agriculture11050387","volume":"11","author":"N Islam","year":"2021","unstructured":"Islam N, Rashid MM, Wibowo S, Xu CY, Morshed A, Wasimi SA, Moore S, Rahman SM (2021) Early weed detection using image processing and machine learning techniques in an Australian Chilli Farm. Agriculture 11(5):387. https:\/\/doi.org\/10.3390\/agriculture11050387","journal-title":"Agriculture"},{"issue":"2","key":"20288_CR48","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s12524-021-01475-7","volume":"50","author":"A Sharifi","year":"2022","unstructured":"Sharifi A, Mahdipour H, Moradi E, Tariq A (2022) Agricultural field extraction with deep learning algorithm and satellite imagery. J Indian Soc Remote Sens 50(2):417\u2013423. https:\/\/doi.org\/10.1007\/s12524-021-01475-7","journal-title":"J Indian Soc Remote Sens"},{"key":"20288_CR49","doi-asserted-by":"publisher","unstructured":"Nikrooz BPD (2020) Application of aerial remote sensing technology for detection of fire blight infected pear trees. Comput Electron Agric 168. https:\/\/doi.org\/10.1016\/j.compag.2019.105147","DOI":"10.1016\/j.compag.2019.105147"},{"key":"20288_CR50","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.compag.2015.05.001","volume":"115","author":"K Tatsumi","year":"2015","unstructured":"Tatsumi K, Yamashiki YA, Torres MA, Taipe CL (2015) Crop classification of upland fields using random forest of time-series landsat 7 ETM+ data. Comput Electron Agric 115:171\u2013179. https:\/\/doi.org\/10.1016\/j.compag.2015.05.001","journal-title":"Comput Electron Agric"},{"key":"20288_CR51","doi-asserted-by":"publisher","unstructured":"Gao Y, Cao Z, Cai W, Gong G, Zhou G, Li L (2023) Apple leaf disease identification in complex background based on BAM-Net. Agronomy 13(5). https:\/\/doi.org\/10.3390\/agronomy13051240","DOI":"10.3390\/agronomy13051240"},{"key":"20288_CR52","doi-asserted-by":"publisher","unstructured":"Qi J, Liu X, Liu K, Xu F, Guo H, Tian X, Li M, Bao Z, Li Y (2022) An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Comput Electron Agric 194. https:\/\/doi.org\/10.1016\/j.compag.2022.106780","DOI":"10.1016\/j.compag.2022.106780"},{"key":"20288_CR53","doi-asserted-by":"publisher","unstructured":"Bao W, Yang X, Liang D, Hu G, Yang X (2021) Lightweight convolutional neural network model for field wheat ear disease identification. Comput Electron Agric 189. https:\/\/doi.org\/10.1016\/j.compag.2021.106367","DOI":"10.1016\/j.compag.2021.106367"},{"key":"20288_CR54","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.rse.2018.08.024","volume":"217","author":"N Zhang","year":"2018","unstructured":"Zhang N, Zhang X, Yang G, Zhu C, Huo L, Feng H (2018) Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sens Environ 217:323\u2013339. https:\/\/doi.org\/10.1016\/j.rse.2018.08.024","journal-title":"Remote Sens Environ"},{"key":"20288_CR55","doi-asserted-by":"publisher","unstructured":"Zhong Y, Hu X, Luo C,Wang X, Zhao J, Zhang L (2020) Whu-hi: uav-borne hyperspdectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CEF. Remote Sens Environ 250. https:\/\/doi.org\/10.1016\/j.rse.2020.112012","DOI":"10.1016\/j.rse.2020.112012"},{"key":"20288_CR56","doi-asserted-by":"publisher","unstructured":"Bento NL, Ferraz GAES, Amorim JDS, Santana LS, Barata RAP, Soares DV, Ferraz PFP (2023) Weed detection and mapping of a coffee farm by a remotely piloted aircraft system. Agronomy 13(830). https:\/\/doi.org\/10.3390\/agronomy13030830","DOI":"10.3390\/agronomy13030830"},{"key":"20288_CR57","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.compag.2018.01.009","journal-title":"Comput Electron Agric"},{"key":"20288_CR58","doi-asserted-by":"publisher","unstructured":"Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1). https:\/\/doi.org\/10.3390\/sym10010011","DOI":"10.3390\/sym10010011"},{"key":"20288_CR59","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272\u2013279. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032","journal-title":"Comput Electron Agric"},{"key":"20288_CR60","doi-asserted-by":"publisher","unstructured":"Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86. https:\/\/doi.org\/10.1016\/j.asoc.2019.105933","DOI":"10.1016\/j.asoc.2019.105933"},{"key":"20288_CR61","doi-asserted-by":"publisher","unstructured":"Atila U, U\u00e7ar M, Akyol K, U\u00e7ar E (2021) Plant leaf disease classification using efficient Net deep learning model. Ecol Inform 61. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101182","DOI":"10.1016\/j.ecoinf.2020.101182"},{"key":"20288_CR62","doi-asserted-by":"crossref","unstructured":"Golpour I, Parian A, Chayjan RA (2014) Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech J Food Sci 32(3):280\u2013287. https:\/\/www.agriculturejournals.cz\/pdfs\/cjf\/2014\/03\/11.pdf","DOI":"10.17221\/238\/2013-CJFS"},{"issue":"12","key":"20288_CR63","doi-asserted-by":"publisher","first-page":"11149","DOI":"10.1016\/j.eswa.2012.03.040","volume":"39","author":"JM Guerrero","year":"2012","unstructured":"Guerrero JM, Pajares G, Montalvo M, Romeo J, Guijarro M (2012) Support vector machines for crop\/weeds identification in maize fields. Expert Syst Appl Int J 39(12):11149\u201311155. https:\/\/doi.org\/10.1016\/j.eswa.2012.03.040","journal-title":"Expert Syst Appl Int J"},{"key":"20288_CR64","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-Boldu 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":"20288_CR65","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"20288_CR66","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, Wu Y, Yue Y (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":"20288_CR67","doi-asserted-by":"publisher","unstructured":"Liu Z, Liu Y, Gao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows.https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"20288_CR68","unstructured":"Wang W, Han C, Zhou T, Liu D (2022) Visual recognition with deep nearest centroids. Comput Sci.\u00a0https:\/\/export.arxiv.org\/abs\/2209.07383v2"},{"key":"20288_CR69","doi-asserted-by":"publisher","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W, Doll\u00e1r P, Girshick R (2023) Segment Anything. 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, pp. 3992\u20134003. https:\/\/doi.org\/10.48550\/arXiv.2304.02643","DOI":"10.48550\/arXiv.2304.02643"},{"key":"20288_CR70","doi-asserted-by":"publisher","first-page":"9811","DOI":"10.1109\/CVPR46437.2021.00969","volume":"2021","author":"D Liu","year":"2021","unstructured":"Liu D, Cui Y, Tan W, Chen Y (2021) SG-Net: Spatial granularity network for one-stage video instance segmentation. IEEE\/CVF Conf Comput Vis Pattern (CVPR) 2021:9811\u20139820. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00969","journal-title":"IEEE\/CVF Conf Comput Vis Pattern (CVPR)"},{"key":"20288_CR71","doi-asserted-by":"publisher","unstructured":"Han C, Wang Y, Cui Y, Cao Z (2023) E^2VPT: An effective and efficient approach for visual prompt tuning. 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), 17445\u201317456. https:\/\/doi.org\/10.48550\/arXiv.2307.13770","DOI":"10.48550\/arXiv.2307.13770"},{"key":"20288_CR72","unstructured":"Han C, Wang Q, Cui Y, Wang W, Huang L, Qi S, Liu D (2024) Facing the elephant in the room: Visual prompt tuning or full finetuning? ArXiv. https:\/\/arxiv.org\/pdf\/2401.12902"},{"key":"20288_CR73","doi-asserted-by":"publisher","unstructured":"Han C, Wang Q, Dianat SA, Rabbani M, Rao RM, Fang Y, Guan Q, Huang L, Liu D (2024) AMD: Automatic multi-step distillation of large-scale vision models. ArXiv, https:\/\/doi.org\/10.48550\/arXiv.2407.04208,\u00a0https:\/\/arxiv.org\/pdf\/2407.04208.pdf","DOI":"10.48550\/arXiv.2407.04208"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20288-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20288-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20288-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T10:16:22Z","timestamp":1753265782000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20288-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":73,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20288"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20288-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"6 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 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":"We confirm that this manuscript is not under consideration elsewhere and that all authors have consented to its submission.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}