{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:00:13Z","timestamp":1785150013406,"version":"3.55.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"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. Fungal Biol."],"abstract":"<jats:p>Early and accurate identification of plant diseases is essential for improving crop productivity and ensuring food security. Many existing deep learning-based plant disease classification methods rely solely on leaf images collected from a controlled environment, which limits their applicability in real-world agricultural conditions where symptoms may be visually unclear and influenced by environmental factors. To address these challenges, this study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification. The proposed architecture employs EfficientNet-B0 for visual feature extraction, BERT for semantic representation of symptom descriptions, and a lightweight multilayer perceptron for modeling environmental factors such as temperature, humidity, rainfall, and soil moisture. Features from all three modalities are fused into a unified representation to train the CNN model. The model is trained and tested upon the Context-Aware Multimodal Augmented PlantVillage dataset covering 38 plant diseases and healthy classes. Experimental results show that AgriFusionNet gives an overall accuracy of 98.94% on the dataset Context-Aware Multimodal Augmented PlantVillage, with competitive precision and recall and F1-score. The multimodal framework facilitates the co-learning of visual, semantic, and contextual environmental representations and the analyses of the confusion matrix and feature interactions give insights into cross-modal relationships. The proposed approach aims to explore context-aware multimodal representation learning for agricultural AI applications, with emphasis on integrating complementary visual, semantic, and contextual information.<\/jats:p>","DOI":"10.3389\/ffunb.2026.1779786","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:10:25Z","timestamp":1785147025000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring"],"prefix":"10.3389","volume":"7","author":[{"given":"Vanmathi","family":"C","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nischitha N","family":"Shetty","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mangayarkarasi","family":"Ramaiah","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selvamuthukumaran","family":"T","sequence":"additional","affiliation":[{"name":"Annamalai University","place":["Annamalai Nagar, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sowbiya","family":"Muneer","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijayalakshmi","family":"Shankar","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"140565","DOI":"10.1109\/ACCESS.2021.3119655","article-title":"Plant disease detection in imbalanced datasets using efficient convolutional neural networks with stepwise transfer learning","volume":"9","author":"Ahmad","year":"2021","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1355941","article-title":"Detection and identification of plant leaf diseases using YOLOv4","volume":"15","author":"Aldakheel","year":"2024","journal-title":"Front. 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