{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:33:00Z","timestamp":1784302380742,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T00:00:00Z","timestamp":1753574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)-ITRC (Information Technology Research Center)","award":["IITP-2024-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2024-RS-2024-00438239"]}]},{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)-ITRC (Information Technology Research Center)","award":["RS-2024-00509257"],"award-info":[{"award-number":["RS-2024-00509257"]}]},{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)","award":["IITP-2024-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2024-RS-2024-00438239"]}]},{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP)","award":["RS-2024-00509257"],"award-info":[{"award-number":["RS-2024-00509257"]}]},{"name":"Institute of Research and Training (IRT) of Hajee Mohammad Danesh Science and Technology University, Bangladesh","award":["IITP-2024-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2024-RS-2024-00438239"]}]},{"name":"Institute of Research and Training (IRT) of Hajee Mohammad Danesh Science and Technology University, Bangladesh","award":["RS-2024-00509257"],"award-info":[{"award-number":["RS-2024-00509257"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks\u2014ResNet50, InceptionV3, and EfficientNetB0\u2014along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics\u2014accuracy, precision, recall, F1-score, confusion matrices, and ROC curves\u2014demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture.<\/jats:p>","DOI":"10.3390\/info16080638","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T09:53:02Z","timestamp":1753696382000},"page":"638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DERIENet: A Deep Ensemble Learning Approach for High-Performance Detection of Jute Leaf Diseases"],"prefix":"10.3390","volume":"16","author":[{"given":"Mst. Tanbin Yasmin","family":"Tanny","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3896-5591","authenticated-orcid":false,"given":"Tangina","family":"Sultana","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"},{"name":"Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Emran","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanchol Kumar","family":"Modok","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjina","family":"Akter","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-2809","authenticated-orcid":false,"given":"Mohammad Shorif","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jahangirnagar University, Dhaka 1342, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6080-9720","authenticated-orcid":false,"given":"Md. Delowar","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"64","DOI":"10.53430\/ijsru.2022.4.1.0088","article-title":"Review on jute leaf: A powerful biological tool","volume":"4","author":"Ahmed","year":"2022","journal-title":"Int. J. Sci. Res. Updat."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Islam, M.M. (2019). Advanced Production Technology and Processing of Jute. 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