{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T18:41:18Z","timestamp":1769798478720,"version":"3.49.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012024","name":"Multimedia University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>\n                    The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach\n                    <jats:italic>LeafSightX<\/jats:italic>\n                    . This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore,\n                    <jats:italic>LeafSightX<\/jats:italic>\n                    integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, \u03c3 = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.\n                  <\/jats:p>","DOI":"10.3389\/frai.2025.1689865","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:41:45Z","timestamp":1769755305000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification"],"prefix":"10.3389","volume":"8","author":[{"given":"Md. Ehsanul","family":"Haque","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, East West University","place":["Dhaka, Bangladesh"]}]},{"given":"Fahmid Al","family":"Farid","sequence":"additional","affiliation":[{"name":"Centre for Image and Vision Computing (CIVC), Faculty of Artificial Intelligence and Engineering, Multimedia University","place":["Cyberjaya, Malaysia"]}]},{"given":"Md. Kamrul","family":"Siam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New York Institute of Technology","place":["New York, NY, United States"]}]},{"given":"Md. Nurul","family":"Absur","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of New York","place":["New York, NY, United States"]}]},{"given":"Jia","family":"Uddin","sequence":"additional","affiliation":[{"name":"AI and Big Data Department, Woosong University","place":["Daejeon, Republic of Korea"]}]},{"given":"Hezerul","family":"Abdul Karim","sequence":"additional","affiliation":[{"name":"Centre for Image and Vision Computing (CIVC), Faculty of Artificial Intelligence and Engineering, Multimedia University","place":["Cyberjaya, Malaysia"]}]}],"member":"1965","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/s40747-024-01764-x","article-title":"A hybrid framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer","volume":"11","author":"Aboelenin","year":"2025","journal-title":"Complex Intell. 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