{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:03:47Z","timestamp":1781006627067,"version":"3.54.1"},"reference-count":106,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116122","type":"journal-article","created":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T21:01:30Z","timestamp":1777842090000},"page":"116122","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning methods for disease detection in cotton fields through UAV imagery: A review"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3021-8119","authenticated-orcid":false,"given":"Anwar","family":"Iqbal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faraz","family":"Kunwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahbaz","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.knosys.2026.116122_bib0001","article-title":"Future of cotton sector in Pakistan: a 2025 outlook","volume":"31","author":"Ashraf","year":"2018","journal-title":"Pak. J. Agric. Res."},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0002","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-62950-y","article-title":"A phenomenological inquiry into farmers\u2019 experiences growing cotton in Punjab, Pakistan","volume":"14","author":"Ashraf","year":"2024","journal-title":"Sci. Rep."},{"issue":"23","key":"10.1016\/j.knosys.2026.116122_bib0003","doi-asserted-by":"crossref","first-page":"29580","DOI":"10.1007\/s11356-020-09222-0","article-title":"Climate change and cotton production: an empirical investigation of Pakistan","volume":"27","author":"Abbas","year":"2020","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"10.1016\/j.knosys.2026.116122_bib0004","doi-asserted-by":"crossref","unstructured":"J.R. Lamichhane, \u201cPlant disease complexes: increasing reports, current knowledge gaps, and future research priorities,\u201d 2025, Elsevier Ltd. doi: 10.1016\/j.cropro.2024.106949.","DOI":"10.1016\/j.cropro.2024.106949"},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0005","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-64601-8","article-title":"Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI","volume":"14","author":"Natarajan","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.116122_bib0006","doi-asserted-by":"crossref","unstructured":"R. Manavalan, \u201cTowards an intelligent approaches for cotton diseases detection: a review,\u201d 2022, Elsevier B.V. doi: 10.1016\/j.compag.2022.107255.","DOI":"10.1016\/j.compag.2022.107255"},{"key":"10.1016\/j.knosys.2026.116122_bib0007","unstructured":"J. Vardhan and K.S. Swetha, \u201cDetection of healthy and diseased crops in drone captured images using deep learning,\u201d 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2305.13490."},{"key":"10.1016\/j.knosys.2026.116122_bib0008","doi-asserted-by":"crossref","unstructured":"H. Slimani, J. El Mhamdi, and A. Jilbab, \u201cDrone-assisted plant disease identification using artificial intelligence: a critical review,\u201d 2023, University of Bahrain. doi:10.12785\/IJCDS\/1401112.","DOI":"10.12785\/ijcds\/1401112"},{"key":"10.1016\/j.knosys.2026.116122_bib0009","doi-asserted-by":"crossref","unstructured":"R. Chin, C. Catal, and A. Kassahun, \u201cPlant disease detection using drones in precision agriculture,\u201d 2023, Springer. doi:10.1007\/s11119-023-10014-y.","DOI":"10.1007\/s11119-023-10014-y"},{"key":"10.1016\/j.knosys.2026.116122_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114891","article-title":"UAV image stitching method based on dual feature guidance and optimal seam","volume":"332","author":"Chen","year":"2026","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.knosys.2026.116122_bib0011","doi-asserted-by":"crossref","unstructured":"M.A. Istiak et al., \u201cAdoption of unmanned aerial vehicle (UAV) imagery in agricultural management: a systematic literature review,\u201d 2023, Elsevier B.V. doi:10.1016\/j.ecoinf.2023.102305.","DOI":"10.1016\/j.ecoinf.2023.102305"},{"key":"10.1016\/j.knosys.2026.116122_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106523","article-title":"Towards automatic field plant disease recognition","volume":"191","author":"Gui","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0013","doi-asserted-by":"crossref","unstructured":"Z.A. Ali, D. Deng, M.K. Shaikh, R. Hasan, and M.A. Khan, \u201cAI-based UAV swarms for monitoring and disease identification of brassica plants using machine learning: a review,\u201d 2024, Tech Science Press. doi:10.32604\/csse.2023.041866.","DOI":"10.32604\/csse.2023.041866"},{"key":"10.1016\/j.knosys.2026.116122_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113253","article-title":"A lightweight object detection method based on fine-grained information extraction and exchange in UAV aerial images","volume":"315","author":"Zhou","year":"2025","journal-title":"Knowl. Based Syst."},{"issue":"5","key":"10.1016\/j.knosys.2026.116122_bib0015","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.3390\/s23052385","article-title":"Vision transformers in image restoration: a survey","volume":"23","author":"Ali","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.knosys.2026.116122_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113760","article-title":"DAF-DETR: a dynamic adaptation feature transformer for enhanced object detection in unmanned aerial vehicles","volume":"323","author":"Song","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.116122_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113449","article-title":"Cross-domain UAV pose estimation: a novel attempt in UAV visual localization","volume":"317","author":"Lin","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.116122_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.gloenvcha.2024.102844","article-title":"Economic factors mediate the impact of drought on farmer suicides in India","volume":"86","author":"Rothler","year":"2024","journal-title":"Glob. Environ. Change"},{"key":"10.1016\/j.knosys.2026.116122_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111925","article-title":"Deep-learning based autonomous-exploration for UAV navigation","volume":"297","author":"Zhao","year":"2024","journal-title":"Knowl. Based Syst."},{"issue":"3","key":"10.1016\/j.knosys.2026.116122_bib0020","first-page":"2917","article-title":"Cotton leaf diseases recognition using deep learning and genetic algorithm","volume":"69","author":"Latif","year":"2021","journal-title":"Comput. Mater. Contin."},{"issue":"3","key":"10.1016\/j.knosys.2026.116122_bib0021","doi-asserted-by":"crossref","first-page":"744","DOI":"10.22214\/ijraset.2022.40731","article-title":"Cotton plant disease prediction using deep learning","volume":"10","author":"Saha","year":"2022","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"10.1016\/j.knosys.2026.116122_bib0022","doi-asserted-by":"crossref","first-page":"134811","DOI":"10.1109\/ACCESS.2022.3232751","article-title":"Handling severity levels of multiple co-occurring cotton plant diseases using improved YOLOX model","volume":"10","author":"Noon","year":"2022","journal-title":"IEEe Access."},{"key":"10.1016\/j.knosys.2026.116122_bib0023","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.compag.2016.07.006","article-title":"In-field cotton detection via region-based semantic image segmentation","volume":"127","author":"Li","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106892","article-title":"Leaf image based plant disease identification using transfer learning and feature fusion","volume":"196","author":"Fan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107484","article-title":"Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model","volume":"203","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0026","doi-asserted-by":"crossref","first-page":"134811","DOI":"10.1109\/ACCESS.2022.3232751","article-title":"Handling severity levels of multiple co-occurring cotton plant diseases using improved YOLOX model","volume":"10","author":"Noon","year":"2022","journal-title":"IEEe Access."},{"key":"10.1016\/j.knosys.2026.116122_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109415","article-title":"Cotton yield prediction utilizing unmanned aerial vehicles (UAV) and Bayesian neural networks","volume":"226","author":"Zhao","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"01","key":"10.1016\/j.knosys.2026.116122_bib0028","article-title":"Forecasting cotton whitefly population using deep learning","volume":"4","author":"Soomro","year":"2022","journal-title":"J. Comput. Biomed. Inform."},{"key":"10.1016\/j.knosys.2026.116122_bib0030","doi-asserted-by":"crossref","first-page":"02","DOI":"10.54393\/fbt.v3i02.40","article-title":"Cotton leaf curl virus (CLCuV): an insight into disaster","author":"Hassan","year":"2023","journal-title":"Futur. Biotechnol."},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0031","article-title":"Multifeature analysis to detect cotton leaf curl virus","volume":"7","author":"Ahmad","year":"2024","journal-title":"JCBI."},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0032","doi-asserted-by":"crossref","DOI":"10.1186\/s13677-023-00582-9","article-title":"Detection of cotton leaf curl disease\u2019s susceptibility scale level based on deep learning","volume":"13","author":"Nazeer","year":"2024","journal-title":"J. Cloud Comput."},{"issue":"3","key":"10.1016\/j.knosys.2026.116122_bib0033","doi-asserted-by":"crossref","first-page":"154","DOI":"10.17221\/105\/2019-PPS","article-title":"Evaluation of the CRISPR\/Cas9 system for the development of resistance against cotton leaf curl virus in model plants","volume":"56","author":"Khan","year":"2020","journal-title":"Plant Prot. Sci."},{"key":"10.1016\/j.knosys.2026.116122_bib0035","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/JRFID.2022.3206841","article-title":"Cotton disease detection based on ConvNeXt and attention mechanisms","volume":"6","author":"Tao","year":"2022","journal-title":"IEEe J. Radio Freq. Identif."},{"key":"10.1016\/j.knosys.2026.116122_bib0034","series-title":"Proceedings of the 5th IEEE International Image Processing, Applications and Systems Conference, IPAS 2022","article-title":"Bacterial blight and cotton leaf curl virus detection using inception V4 based CNN model for cotton crops","author":"Anwar","year":"2022"},{"issue":"2","key":"10.1016\/j.knosys.2026.116122_bib0036","first-page":"143","article-title":"Assessment of the impact of climate change on the productivity of cotton: empirical evidence from cotton zone, southern Punjab, Pakistan","volume":"9","author":"Ahmad","year":"2021","journal-title":"Int. J. Agric. Extens."},{"issue":"4","key":"10.1016\/j.knosys.2026.116122_bib0037","doi-asserted-by":"crossref","first-page":"15813","DOI":"10.48084\/etasr.7535","article-title":"An advanced deep learning approach for precision diagnosis of cotton leaf diseases: a multifaceted agricultural technology solution","volume":"14","author":"Nagarjun","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"10.1016\/j.knosys.2026.116122_bib0038","first-page":"4273","article-title":"A review on UAV-based applications for plant disease detection and monitoring","volume":"15","author":"Kouadio","year":"2023","journal-title":"Multidiscip. Digit. Publ. Inst. (MDPI)"},{"key":"10.1016\/j.knosys.2026.116122_bib0039","doi-asserted-by":"crossref","unstructured":"L. Epifani and A. Caruso, \u201cA survey on deep learning in UAV imagery for precision agriculture and wild flora monitoring: datasets, models and challenges,\u201d 2024, Elsevier B.V.9, 100625, 10.1016\/j.atech.2024.100625.","DOI":"10.1016\/j.atech.2024.100625"},{"key":"10.1016\/j.knosys.2026.116122_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2024.102846","article-title":"Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery","volume":"84","author":"Xu","year":"2024","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.knosys.2026.116122_bib0041","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ymeth.2022.09.007","article-title":"An interpretable deep learning model for classifying adaptor protein complexes from sequence information","volume":"207","author":"Kha","year":"2022","journal-title":"Methods"},{"key":"10.1016\/j.knosys.2026.116122_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105456","article-title":"An optimized dense convolutional neural network model for disease recognition and classification in corn leaf","volume":"175","author":"Waheed","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"16","key":"10.1016\/j.knosys.2026.116122_bib0043","doi-asserted-by":"crossref","first-page":"25307","DOI":"10.1007\/s11042-023-14933-w","article-title":"Classification of diseased cotton leaves and plants using improved deep convolutional neural network","volume":"82","author":"Rai","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.knosys.2026.116122_bib0044","doi-asserted-by":"crossref","first-page":"140565","DOI":"10.1109\/ACCESS.2021.3119655","article-title":"Plant disease detection in imbalanced datasets using efficient convolutional neural networks with stepwise transfer learning","volume":"9","author":"Ahmad","year":"2021","journal-title":"IEEe Access."},{"key":"10.1016\/j.knosys.2026.116122_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.104948","article-title":"Depthwise separable convolution architectures for plant disease classification","volume":"165","author":"KC","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107484","article-title":"Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model","volume":"203","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0047","series-title":"Proceedings of the IEEE 7th Conference on Information and Communication Technology, CICT 2023","article-title":"Drone-based weed and disease detection In agricultural fields to maximize crop health using a Yolov8 approach","author":"Pavithra","year":"2023"},{"key":"10.1016\/j.knosys.2026.116122_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112655","article-title":"Advanced drone-based weed detection using feature-enriched deep learning approach","volume":"305","author":"Rehman","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.116122_bib0049","article-title":"Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial imagery","volume":"4","author":"Moazzam","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.knosys.2026.116122_bib0050","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/JSTARS.2023.3234161","article-title":"A CNN-transformer hybrid model based on CSWin transformer for UAV image object detection","volume":"16","author":"Lu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116122_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108434","article-title":"Pixel-level regression for UAV hyperspectral images: deep learning-based quantitative inverse of wheat stripe rust disease index","volume":"215","author":"Deng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0052","first-page":"9511","article-title":"Deep learning techniques to classify agricultural crops through UAV imagery: a review","volume":"34","author":"Bouguettaya","year":"2022","journal-title":"Springer Sci. Bus. Media Deutschl. GmbH"},{"key":"10.1016\/j.knosys.2026.116122_bib0053","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1007\/s10462-023-10595-0","article-title":"A survey of the vision transformers and their CNN-transformer based variants","volume":"56","author":"Khan","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.knosys.2026.116122_bib0054","first-page":"287","article-title":"A comprehensive survey of transformers for computer vision","volume":"7","author":"Jamil","year":"2023","journal-title":"Multidiscip. Digit. Publ. Inst. (MDPI)"},{"key":"10.1016\/j.knosys.2026.116122_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105711","article-title":"Evaluation of cotton emergence using UAV-based imagery and deep learning","volume":"177","author":"Feng","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"5","key":"10.1016\/j.knosys.2026.116122_bib0056","doi-asserted-by":"crossref","first-page":"11561","DOI":"10.48084\/etasr.6187","article-title":"Performance analysis of deep transfer learning models for the automated detection of cotton plant diseases","volume":"13","author":"Anwar","year":"2023","journal-title":"Eng. Technol. Appl. Sci. Res."},{"issue":"2","key":"10.1016\/j.knosys.2026.116122_bib0057","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3390\/agriculture14020247","article-title":"Intelligent cotton pest and disease detection: edge computing solutions with transformer technology and knowledge graphs","volume":"14","author":"Gao","year":"2024","journal-title":"Agriculture"},{"key":"10.1016\/j.knosys.2026.116122_bib0058","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106629","article-title":"GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing","volume":"193","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0060","series-title":"Proceedings of the ICWITE2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship","first-page":"625","article-title":"Early cotton plant disease detection using drone monitoring and deep learning","author":"Jayanthy","year":"2024"},{"key":"10.1016\/j.knosys.2026.116122_bib0061","first-page":"5","article-title":"Cotton crop disease detection on remotely collected aerial images with deep learning","author":"Qian","year":"2022","journal-title":"SPIE-Intl. Soc. Optical Eng."},{"key":"10.1016\/j.knosys.2026.116122_bib0062","series-title":"Proceedings of the Second International Conference on Networks, Multimedia and Information Technology (NMITCON)","first-page":"1","article-title":"Cotton leaf disease detection using transfer learning","author":"Prashanth","year":"2024"},{"key":"10.1016\/j.knosys.2026.116122_bib0063","series-title":"Proceedings of the International Conference on Disruptive Technologies, ICDT 2023","first-page":"559","article-title":"Cotton leaf disease classification using deep learning based novel approach","author":"Pandey","year":"2023"},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0064","article-title":"A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques","volume":"10","author":"Ahmad","year":"2024","journal-title":"Cogent. Food Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109467","article-title":"Real-time field disease identification based on a lightweight model","volume":"226","author":"Quan","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"16","key":"10.1016\/j.knosys.2026.116122_bib0066","doi-asserted-by":"crossref","first-page":"25307","DOI":"10.1007\/s11042-023-14933-w","article-title":"Classification of diseased cotton leaves and plants using improved deep convolutional neural network","volume":"82","author":"Rai","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.knosys.2026.116122_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105711","article-title":"Evaluation of cotton emergence using UAV-based imagery and deep learning","volume":"177","author":"Feng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0068","series-title":"Proceedings of the ICWITE2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship","first-page":"625","article-title":"Early cotton plant disease detection using drone monitoring and deep learning","author":"Jayanthy","year":"2024"},{"key":"10.1016\/j.knosys.2026.116122_bib0069","first-page":"10433","article-title":"Drone-assisted plant disease identification using artificial intelligence: a critical review","volume":"14","author":"Slimani","year":"2023","journal-title":"Univ. Bahrain"},{"key":"10.1016\/j.knosys.2026.116122_bib0070","series-title":"Proceedings of the International Conference on Engineering Technologies and Applied Sciences: Shaping the Future of Technology through Smart Computing and Engineering, ICETAS 2023","article-title":"Predicting temperature and humidity for cotton field using deep learning models in smart agriculture system","author":"Shahid","year":"2023"},{"key":"10.1016\/j.knosys.2026.116122_bib0071","article-title":"Cotton leaf diseases detection and prediction using RESNET algorithm","volume":"54","author":"Pavani","year":"2022","journal-title":"J. Harbin Inst. Technol."},{"key":"10.1016\/j.knosys.2026.116122_bib0072","article-title":"Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery","volume":"497","author":"Yu","year":"2021","journal-title":"For. Ecol. Manage."},{"key":"10.1016\/j.knosys.2026.116122_bib0073","series-title":"Proceedings of the 2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024 - Proceedings","article-title":"Real time cotton crop disease detection using deep transfer learning","author":"Chavan","year":"2024"},{"key":"10.1016\/j.knosys.2026.116122_bib0074","series-title":"Proceedings of the 12th IEEE International Conference on Advanced Computing, ICoAC 2023","article-title":"SwinCNN: a hybrid deep learning architecture for accurate cotton disease prediction","author":"Muthurajkumar","year":"2023"},{"key":"10.1016\/j.knosys.2026.116122_bib0075","series-title":"Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023","first-page":"1601","article-title":"Cotton crop disease detection using MobileNet snapshot ensemble technique and vision transformers","author":"Gudela","year":"2023"},{"key":"10.1016\/j.knosys.2026.116122_bib0076","first-page":"5","article-title":"Cotton crop disease detection on remotely collected aerial images with deep learning","author":"Qian","year":"2022","journal-title":"SPIE-Intl. Soc. Optical Eng."},{"key":"10.1016\/j.knosys.2026.116122_bib0077","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106629","article-title":"GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing","volume":"193","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"10.1016\/j.knosys.2026.116122_bib0078","first-page":"1277","article-title":"Interaction of meloidogyne incognita and fungi: a potential threat to cotton crop in Punjab","volume":"58","author":"Naz","year":"2021","journal-title":"Pak. J. Agric. Sci."},{"issue":"4","key":"10.1016\/j.knosys.2026.116122_bib0079","first-page":"47","article-title":"Leveraging convolutional neural network and transfer learning for cotton plant and leaf disease recognition","volume":"13","author":"Ahmed","year":"2021","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"10.1016\/j.knosys.2026.116122_bib0080","first-page":"1","article-title":"Deep learning-based image processing for cotton leaf disease and pest diagnosis","volume":"2021","author":"Azath","year":"2021","journal-title":"J. Electr. Comput. Eng."},{"key":"10.1016\/j.knosys.2026.116122_bib0081","article-title":"Real-time precision spraying application for tobacco plants","volume":"8","author":"Arsalan","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.knosys.2026.116122_bib0082","first-page":"1","author":"Sreeja","year":"2022","journal-title":"Cotton Plant Disease Prediction using Deep Learning, 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)"},{"key":"10.1016\/j.knosys.2026.116122_bib0083","series-title":"Improvement of Plant Production in the Era of Climate Change","first-page":"95","article-title":"Climate change and cotton production","author":"Iqbal","year":"2022"},{"key":"10.1016\/j.knosys.2026.116122_bib0084","doi-asserted-by":"crossref","first-page":"112708","DOI":"10.1109\/ACCESS.2020.3002948","article-title":"A multi-modal approach for crop health mapping using low altitude remote sensing, Internet of things (IoT) and machine learning","volume":"8","author":"Shafi","year":"2020","journal-title":"IEEe Access."},{"key":"10.1016\/j.knosys.2026.116122_bib0085","series-title":"Proceedings of the 12th International Conference on Electrical and Computer Engineering, ICECE 2022","first-page":"413","article-title":"Cotton leaf disease detection and classification using lightweight CNN architecture","author":"Peyal","year":"2022"},{"key":"10.1016\/j.knosys.2026.116122_bib0086","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109467","article-title":"Real-time field disease identification based on a lightweight model","volume":"226","author":"Quan","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"6","key":"10.1016\/j.knosys.2026.116122_bib0087","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s11119-021-09808-9","article-title":"Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer","volume":"22","author":"Khan","year":"2021","journal-title":"Precis. Agric."},{"issue":"27","key":"10.1016\/j.knosys.2026.116122_bib0088","doi-asserted-by":"crossref","first-page":"42277","DOI":"10.1007\/s11042-023-15221-3","article-title":"Survey on crop pest detection using deep learning and machine learning approaches","volume":"82","author":"Chithambarathanu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.knosys.2026.116122_bib0089","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106468","article-title":"T-CNN: trilinear convolutional neural networks model for visual detection of plant diseases","volume":"190","author":"Wang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0091","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106184","article-title":"Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning","volume":"186","author":"Jiang","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"14","key":"10.1016\/j.knosys.2026.116122_bib0092","doi-asserted-by":"crossref","first-page":"14628","DOI":"10.1109\/JSEN.2022.3182304","article-title":"Lightweight inception networks for the recognition and detection of rice plant diseases","volume":"22","author":"Chen","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.knosys.2026.116122_bib0093","article-title":"Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection","volume":"15","author":"Zhu","year":"2024","journal-title":"Front. Media SA"},{"key":"10.1016\/j.knosys.2026.116122_bib0094","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106543","article-title":"An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network","volume":"192","author":"Pandey","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105749","article-title":"H2K \u2013 A robust and optimum approach for detection and classification of groundnut leaf diseases","volume":"178","author":"Suganya Devi","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0096","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3906\/elk-2004-4","article-title":"Deep-learning-based spraying area recognition system for unmanned-aerial-vehicle-based sprayers","volume":"29","author":"Khan","year":"2021","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"10.1016\/j.knosys.2026.116122_bib0097","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106892","article-title":"Leaf image based plant disease identification using transfer learning and feature fusion","volume":"196","author":"Fan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0098","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112655","article-title":"Advanced drone-based weed detection using feature-enriched deep learning approach","volume":"305","author":"Rehman","year":"2024","journal-title":"Knowl. Based Syst."},{"issue":"7","key":"10.1016\/j.knosys.2026.116122_bib0099","article-title":"From pixels to plant health: accurate detection of banana xanthomonas wilt in complex African landscapes using high-resolution UAV images and deep learning","volume":"6","author":"Mora","year":"2024","journal-title":"Discover Appl. Sci."},{"issue":"6","key":"10.1016\/j.knosys.2026.116122_bib0100","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s11119-021-09808-9","article-title":"Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer","volume":"22","author":"Khan","year":"2021","journal-title":"Precis. Agric."},{"issue":"1","key":"10.1016\/j.knosys.2026.116122_bib0101","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11042-022-13144-z","article-title":"VGG-ICNN: a lightweight CNN model for crop disease identification","volume":"82","author":"Thakur","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.knosys.2026.116122_bib0102","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109719","article-title":"Research on unmanned aerial vehicle (UAV) rice field weed sensing image segmentation method based on CNN-transformer","volume":"229","author":"Guo","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0103","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106271","article-title":"LASSR: effective super-resolution method for plant disease diagnosis","volume":"187","author":"Cap","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.knosys.2026.116122_bib0104","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105093","article-title":"Crop conditional convolutional neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions","volume":"167","author":"Picon","year":"2019","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.knosys.2026.116122_bib0105","first-page":"191","article-title":"Synchronous federated learning based multi unmanned aerial vehicles for secure applications","volume":"24","author":"Sharma","year":"2023","journal-title":"Scal. Comput."},{"key":"10.1016\/j.knosys.2026.116122_bib0106","series-title":"Proceedings of the IEEE Conference on Technologies for Sustainability, SusTech 2024","first-page":"356","article-title":"Generative AI-based land cover classification via federated learning CNNs: sustainable insights from UAV Imagery","author":"Jockusch","year":"2024"},{"issue":"24","key":"10.1016\/j.knosys.2026.116122_bib0107","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.3390\/math10244735","article-title":"Semantic segmentation of UAV images based on transformer framework with context information","volume":"10","author":"Kumar","year":"2022","journal-title":"Mathematics"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008488?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008488?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:32:46Z","timestamp":1781004766000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":106,"alternative-id":["S0950705126008488"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116122","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning methods for disease detection in cotton fields through UAV imagery: A review","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116122","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116122"}}