{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T08:48:17Z","timestamp":1782118097211,"version":"3.54.5"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% \u00b1 0.10%, average validation accuracy of 98.28% \u00b1 0.20%, and average test accuracy of 99.07% \u00b1 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.<\/jats:p>","DOI":"10.3390\/jimaging9020053","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T02:30:35Z","timestamp":1676946635000},"page":"53","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2551-3163","authenticated-orcid":false,"given":"Mohan","family":"Bhandari","sequence":"first","affiliation":[{"name":"Department of Science and Technology, Samriddhi College, Bhaktapur 44800, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0616-3180","authenticated-orcid":false,"given":"Tej Bahadur","family":"Shahi","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Norman Gardens, Rockhampton 4701, Australia"},{"name":"Central Department of Computer Science and IT, Tribhuvan University, Kathmandu 44600, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1010-7552","authenticated-orcid":false,"given":"Arjun","family":"Neupane","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Norman Gardens, Rockhampton 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-8622","authenticated-orcid":false,"given":"Kerry Brian","family":"Walsh","sequence":"additional","affiliation":[{"name":"Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1094\/PDIS-92-4-0530","article-title":"Visual rating and the use of image analysis for assessing different symptoms of citrus canker on grapefruit leaves","volume":"92","author":"Bock","year":"2008","journal-title":"Plant Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1755-1315\/951\/1\/012002","article-title":"Traditional and current-prospective methods of agricultural plant diseases detection: A review","volume":"951","author":"Khakimov","year":"2022","journal-title":"IOP Conf. 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