{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:54:33Z","timestamp":1783619673840,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002724","name":"American University of Sharjah","doi-asserted-by":"publisher","award":["URG21-001"],"award-info":[{"award-number":["URG21-001"]}],"id":[{"id":"10.13039\/501100002724","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (\u201cAgroAId\u201d), which incorporates computer vision technology to classify a plant\u2019s [species\u2013disease] combination from an input plant leaf image, recognizing 39 [species-and-disease] classes. Our method comprises a comparative analysis to maximize our multi-label classification model\u2019s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs\u2014MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0\u2014and tested four transfer learning scenarios (percentage of frozen-vs.-retrained base layers): (1) freezing all convolutional layers; (2) freezing 80% of layers; (3) freezing 50% only; and (4) retraining all layers. A total of 32 model variations are built and assessed using standard metrics (accuracy, F1-score, confusion matrices). The most lightweight, high-accuracy model is concluded to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, achieving 99% accuracy and demonstrating the efficacy of the proposed approach; it is integrated into our plant care support system in a TensorFlow Lite format alongside the front-end mobile application and centralized cloud database. Finally, our system also uses the collective user classification data to generate spatiotemporal analytics about regional and seasonal disease trends, making these analytics accessible to all system users to increase awareness of global agricultural trends.<\/jats:p>","DOI":"10.3390\/informatics9030055","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T04:59:16Z","timestamp":1658897956000},"page":"55","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite"],"prefix":"10.3390","volume":"9","author":[{"given":"Mariam","family":"Reda","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rawan","family":"Suwwan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seba","family":"Alkafri","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yara","family":"Rashed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamer","family":"Shanableh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lu, J., Tan, L., and Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. 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Available online: https:\/\/www.tensorflow.org\/lite\/models\/convert."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/3\/55\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:40Z","timestamp":1760140600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/3\/55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["informatics9030055"],"URL":"https:\/\/doi.org\/10.3390\/informatics9030055","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,26]]}}}