{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:04:46Z","timestamp":1777637086097,"version":"3.51.4"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Techniques<\/jats:title>\n                    <jats:p>Three deep learning architectures\u2014a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)\u2014are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes\u2014angular leaf spot, bean rust, and healthy leaves\u2014was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcomes<\/jats:title>\n                    <jats:p>ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>In conclusion<\/jats:title>\n                    <jats:p>The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1703449","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:02:07Z","timestamp":1777442527000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative deep learning approaches for bean leaf disease recognition"],"prefix":"10.3389","volume":"17","author":[{"given":"Kollipara","family":"Anirudh","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darbha","family":"Srujan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Sai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gnana Swathika","family":"O. 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