Vision Transformer-Based Systems for Crop Disease Detection and Monitoring in Precision Agriculture

Vision Transformer-Based Systems for Crop Disease Detection and Monitoring in Precision Agriculture | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 May 2025 V1 Latest version Share on Vision Transformer-Based Systems for Crop Disease Detection and Monitoring in Precision Agriculture Author : kayode sheriffdeen 0009-0009-1587-4038 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174741485.56785180/v1 211 views 107 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The integration of advanced deep learning models into precision agriculture has the potential to significantly enhance crop health monitoring and disease management. This research explores the application of Vision Transformer (ViT)-based systems for the detection and monitoring of crop diseases, addressing the limitations of conventional Convolutional Neural Networks (CNNs) in capturing long-range dependencies and global contextual information. We propose a ViT-driven framework that leverages highresolution aerial and ground-level imagery to accurately identify a wide range of plant diseases across multiple crop types. The system is trained and evaluated on benchmark agricultural datasets and fieldcollected images, demonstrating superior performance in classification accuracy, robustness to image variability, and early-stage disease detection compared to traditional CNN architectures. Additionally, we incorporate an attention-based interpretability module to provide visual explanations, aiding agronomists in decision-making processes. Our findings highlight the potential of ViT-based models in transforming agricultural practices by enabling scalable, real-time crop monitoring and proactive disease management, thereby contributing to sustainable farming and food security. Supplementary Material File (vision transformer.pdf) Download 203.44 KB Information & Authors Information Version history V1 Version 1 16 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords crop disease crop disease prediction Authors Affiliations kayode sheriffdeen 0009-0009-1587-4038 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 211 views 107 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation kayode sheriffdeen. Vision Transformer-Based Systems for Crop Disease Detection and Monitoring in Precision Agriculture. Authorea . 16 May 2025. DOI: https://doi.org/10.22541/au.174741485.56785180/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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