{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T19:19:59Z","timestamp":1790018399158,"version":"4.0.1"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004686","name":"Deanship of Scientific Research, King Faisal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cashew farms are highly vulnerable to the attack of pests and plant diseases, which leads to massive losses in production and economic value. To diagnose the cashew disease, recent studies have explored deep learning methods; however, the existing models often have poor representative features and low generaliz-ability to different situations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>As part of these solutions, we suggest a hybrid late-fusion architecture (combining EfficientNetV2-M and MobileNetV3-S) to extract the features and train them with XGBoost and CatBoost to give the classification, using the BOHB optimisation to make the hyper-parameter choices. By combin-ing MobileNetV3-S and EfficientNetV2-M, we can benefit from fine-grained visual information and computer efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result and discussion<\/jats:title>\n                    <jats:p>The proposed model was experimented on the CCMT dataset comprising Anthracnose, Gummosis, Leaf Miner, Red Rust, and healthy leaf samples, achieving classification accuracies of 90% and 93%, with reduced computation times of 0.20 seconds for the XGBoost classifier and 0.07 seconds for the CatBoost classifier. As our findings show, boosting-based classifiers and efficient backbone networks can be combined to identify cashew disease effectively and computationally less complex.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1815033","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T06:09:41Z","timestamp":1779430181000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimised hybrid late fusion deep learning model for cashew disease classification"],"prefix":"10.3389","volume":"17","author":[{"given":"Meenakshi","family":"K","sequence":"first","affiliation":[{"name":"Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology","place":["Kattankulathur, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh","family":"Sankaranarayanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University","place":["Al-Ahsa, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"B3","first-page":"862","article-title":"Classification of insect pest species using CNN based models","author":"Agarwal","year":"2023"},{"key":"B19","doi-asserted-by":"publisher","first-page":"31103","DOI":"10.1109\/ACCESS.2022.3159678","article-title":"End-to-end deep learning model for corn leaf disease classification","volume":"10","author":"Amin","year":"2022","journal-title":"IEEE Access"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2328","DOI":"10.1016\/j.procs.2023.01.208","article-title":"Exploring deep ensemble model for insect and pest detection from images","volume":"218","author":"Anwar","year":"2023","journal-title":"Proc. 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