{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:47:30Z","timestamp":1784216850754,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The frequent emergence of multiple diseases in tomato plants poses a significant challenge to agriculture, requiring innovative solutions to deal with this problem. The paper explores the application of machine learning (ML) technologies to develop a model capable of identifying and classifying diseases in tomato leaves. Our work involved the implementation of a custom convolutional neural network (CNN) trained on a diverse dataset of tomato leaf images. The performance of the proposed CNN model was evaluated and compared against the performance of existing pre-trained CNN models, i.e., the VGG16 and VGG19 models, which are extensively used for image classification tasks. The proposed CNN model was further tested with images of tomato leaves captured from a real-world garden setting in Greece. The captured images were carefully preprocessed and an in-depth study was conducted on how either each image preprocessing step or a different\u2014not supported by the dataset used\u2014strain of tomato affects the accuracy and confidence in detecting tomato leaf diseases.<\/jats:p>","DOI":"10.3390\/info16030231","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T06:36:23Z","timestamp":1742193383000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["From Pixels to Diagnosis: Implementing and Evaluating a CNN Model for Tomato Leaf Disease Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Zamir","family":"Osmenaj","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of the Peloponnese, 221 31 Tripoli, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgenia-Maria","family":"Tseliki","sequence":"additional","affiliation":[{"name":"Department of Informatics, Athens University of Economics Business, 104 34 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia H.","family":"Kapellaki","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of the Peloponnese, 221 31 Tripoli, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Tselikis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, University of West Attica, 122 41 Athens-Egaleo, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5799-3558","authenticated-orcid":false,"given":"Nikolaos D.","family":"Tselikas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of the Peloponnese, 221 31 Tripoli, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Talaei Khoei, T., and Kaabouch, N. (2023). Machine Learning: Models, Challenges, and Research Directions. Future Internet, 15.","DOI":"10.3390\/fi15100332"},{"key":"ref_2","unstructured":"De Andrade, A. (2019). Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.34133\/plantphenomics.0042","article-title":"A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet","volume":"5","author":"Tang","year":"2023","journal-title":"Plant Phenomics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18568","DOI":"10.1038\/s41598-022-21498-5","article-title":"A robust deep learning approach for tomato plant leaf disease localization and classification","volume":"12","author":"Nawaz","year":"2022","journal-title":"Sci Rep."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, E., Zhang, N., Li, F., and Lv, C. (2024). 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Sci."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/3\/231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:54:31Z","timestamp":1760028871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/3\/231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,16]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["info16030231"],"URL":"https:\/\/doi.org\/10.3390\/info16030231","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,16]]}}}