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Rapid, accurate detection remains a challenge, particularly in resource\u2010constrained environments lacking portable tools. Our contribution, Agri Bot, introduces a pioneering deep convolutional neural network (CNN) model, uniquely optimized for mobile deployment, transforming plant disease diagnosis. This novel model integrates a lightweight architecture with advanced feature extraction, achieving an exceptional 97.30% accuracy and 98.76% area under the curve (AUC). Unlike computationally intensive traditional CNNs, Agri Bot\u2019s innovative design\u2014featuring a hybrid convolutional autoencoder, max pooling, and dropout layers\u2014ensures high\u2010speed, real\u2010time performance on mobile devices. Comparative studies reveal Agri Bot\u2019s superiority, surpassing state\u2010of\u2010the\u2010art models like VGG16 (71.48%) and ResNet50 (96.46%), while rivaling InceptionV3 (99.07%) with significantly lower computational demands. By delivering precise, accessible diagnostics to remote regions, Agri Bot revolutionizes agricultural disease management, enhancing crop resilience and global food security.<\/jats:p>","DOI":"10.1155\/int\/7644407","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T06:48:45Z","timestamp":1748587725000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Convolutional Neural Networks for Plant Disease Detection: A Mobile Application Approach (Agri Bot)"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9437-8367","authenticated-orcid":false,"given":"Hegazi","family":"Ibrahim","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3379-7314","authenticated-orcid":false,"given":"Abdelmoty M.","family":"Ahmed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7292-6345","authenticated-orcid":false,"given":"Belgacem","family":"Bouallegue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7248-871X","authenticated-orcid":false,"given":"Mahmoud M.","family":"Khattab","sequence":"additional","affiliation":[]},{"given":"Mohab Abd","family":"El-Fattah","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2232-1035","authenticated-orcid":false,"given":"Nesma Abd","family":"El-Mawla","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12193188"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.phyto.43.113004.133839"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.07.070"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2019.02.002"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14970-5"},{"key":"e_1_2_10_6_2","first-page":"3897","article-title":"Machine Learning and Deep Learning: A Review of Methods and Applications","volume":"10","author":"Sharifani K.","year":"2023","journal-title":"World Information Technology and Engineering Journal"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogr.2023.04.001"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29230-7"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2023.07.001"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/02564602.2022.2121772"},{"key":"e_1_2_10_11_2","doi-asserted-by":"crossref","unstructured":"HuJ. 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