{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T03:01:25Z","timestamp":1783134085942,"version":"3.54.6"},"reference-count":40,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["WEB"],"published-print":{"date-parts":[[2024,9,13]]},"abstract":"<jats:p>Underground crop leave disease classification is the most significant area in the agriculture sector as they are the significant source of carbohydrates for human food. However, a disease-ridden plant could threaten the availability of food for millions of people. Researchers tried to use computer vision (CV) to develop an image classification algorithm that might warn farmers by clicking the images of plant\u2019s leaves to find if the crop is diseased or not. This work develops anew DHCLDC model for underground crop leave disease classification that considers the plants like cassava, potato and groundnut. Here, preprocessing is done by employing median filter, followed by segmentation using Improved U-net (U-Net with nested convolutional block). Further, the features extracted comprise of color features, shape features and improved multi text on (MT) features. Finally, Hybrid classifier (HC) model is developed for DHCLDC, which comprised CNN and LSTM models. The outputs from HC(CNN\u00a0+ LSTM) are then given for improved score level fusion (SLF) from which final detected e are attained. Finally, simulations are done with 3 datasets to show the betterment of HC (CNN\u00a0+ LSTM) based DHCLDC model. The specificity of HC (CNN\u00a0+ LSTM) is high, at 95.41, compared to DBN, NN, RF, KNN, CNN, LSTM, DCNN, and SVM.<\/jats:p>","DOI":"10.3233\/web-230180","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T10:54:35Z","timestamp":1706871275000},"page":"443-465","source":"Crossref","is-referenced-by-count":2,"title":["Deep hybrid classification model for leaf disease classification of underground crops"],"prefix":"10.1177","volume":"22","author":[{"given":"R.","family":"Salini","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai -602105, Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Charlyn Pushpa Latha","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai -602105, Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmita","family":"Khilar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai -602105, Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/WEB-230180_ref2","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"},{"issue":"4","key":"10.3233\/WEB-230180_ref3","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1109\/JSTARS.2017.2788426","article-title":"Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets","volume":"11","author":"AlSuwaidi","year":"2018","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"10.3233\/WEB-230180_ref4","doi-asserted-by":"crossref","unstructured":"A.O.\u00a0Anim-Ayeko, C.\u00a0Schillaci and A.\u00a0Lipani, Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning, Smart Agricultural Technology 10 (2023), 100178.","DOI":"10.1016\/j.atech.2023.100178"},{"key":"10.3233\/WEB-230180_ref5","doi-asserted-by":"crossref","unstructured":"C.\u00a0Bizabani, S.J.\u00a0Rogans and M.E.\u00a0Rey, Differential miRNA profiles in South African cassava mosaic virus-infected cassava landraces reveal clues to susceptibility and tolerance to cassava mosaic disease, Virus Research 303 (2021), 198400.","DOI":"10.1016\/j.virusres.2021.198400"},{"issue":"1","key":"10.3233\/WEB-230180_ref6","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s12985-021-01572-6","article-title":"Development of a triple antibody sandwich enzyme-linked immunosorbent assay for cassava mosaic disease detection using a monoclonal antibody to Sri Lankan cassava mosaic virus","volume":"18","author":"Charoenvilaisiri","year":"2021","journal-title":"Virology Journal"},{"key":"10.3233\/WEB-230180_ref7","first-page":"4031","article-title":"EfficientNet: A low-bandwidth iot image sensor framework for cassava leaf disease classification","volume":"33","author":"Chen","year":"2021","journal-title":"Sens. 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