{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:31Z","timestamp":1777890031343,"version":"3.51.4"},"reference-count":41,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["WEB"],"published-print":{"date-parts":[[2024,4,22]]},"abstract":"<jats:p>Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.<\/jats:p>","DOI":"10.3233\/web-230015","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:26:14Z","timestamp":1688124374000},"page":"209-230","source":"Crossref","is-referenced-by-count":1,"title":["SMoGW-based deep CNN: Plant disease detection and classification using SMoGW-deep CNN classifier"],"prefix":"10.1177","volume":"22","author":[{"given":"Archana Buddham","family":"Pahurkar","sequence":"first","affiliation":[{"name":"Electronics and Telecommunication, Prof. Ram Meghe Institute of Technology and Research Badnera, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravindra Madhukarrao","family":"Deshmukh","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunication, Dr. Rajendra Gode Institute of Technology and Research Amravati, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/WEB-230015_ref1","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","article-title":"Plant disease identification from individual lesions and spots using deep learning","volume":"180","author":"Arnal Barbedo","year":"2019","journal-title":"Biosystems Eng."},{"key":"10.3233\/WEB-230015_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105393"},{"key":"10.3233\/WEB-230015_ref3","doi-asserted-by":"crossref","unstructured":"J.\u00a0Chen, H.\u00a0Yin and D.\u00a0Zhang, A self-adaptive classification method for plant disease detection using GMDH-logistic model, Sustainable Computing: Informatics and Systems 28 (2020), 100415.","DOI":"10.1016\/j.suscom.2020.100415"},{"key":"10.3233\/WEB-230015_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09813-w"},{"key":"10.3233\/WEB-230015_ref5","doi-asserted-by":"publisher","first-page":"55724","DOI":"10.1109\/ACCESS.2020.2982055","article-title":"Crop leaf disease image super-resolution and identification with dual attention and topology fusion generative adversarial network","volume":"8","author":"Dai","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"10.3233\/WEB-230015_ref6","first-page":"189","article-title":"Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters","volume":"4","author":"Faithpraise","year":"2013","journal-title":"Int. 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