{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:35:26Z","timestamp":1792100126993,"version":"4.4.0"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biotechnology"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.jbiotec.2023.07.011","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T23:02:19Z","timestamp":1691103739000},"page":"101-113","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":53,"special_numbering":"C","title":["Tomato plant leaf disease segmentation and multiclass disease detection using hybrid optimization enabled deep learning"],"prefix":"10.1016","volume":"374","author":[{"given":"Manjunatha","family":"Badiger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Alex","family":"Mathew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jbiotec.2023.07.011_bib1","first-page":"81","volume":"vol.1","author":"Adhikari","year":"2018","journal-title":"\"Tomato Plant Dis. Detect. Syst.\""},{"key":"10.1016\/j.jbiotec.2023.07.011_bib2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","article-title":"ToLeD: tomato leaf disease detection using convolution neural network","volume":"vol.167","author":"Agarwal","year":"2020","journal-title":"Procedia Comput. Sci."},{"issue":"5","key":"10.1016\/j.jbiotec.2023.07.011_bib3","doi-asserted-by":"crossref","DOI":"10.3390\/agriculture13050936","article-title":"Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification","volume":"vol.13","author":"Aggarwal","year":"2023","journal-title":"agriculture"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib4","doi-asserted-by":"crossref","unstructured":"Alaeddine, H. and Jihene, M., \"Deep Batch-normalized eLUAlexNet For Plant Diseases Classification\", In 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 17\u201322, March 2021.","DOI":"10.1109\/SSD52085.2021.9429404"},{"issue":"2","key":"10.1016\/j.jbiotec.2023.07.011_bib5","article-title":"A deep learning-based novel approach for weed growth estimation","volume":"vol.31","author":"Anand Muni Mishra","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib6","unstructured":"Ashqar, B.A. and Abu-Naser, S.S., \"Image-based tomato leaves diseases detection using deep learning\", 2018."},{"issue":"4","key":"10.1016\/j.jbiotec.2023.07.011_bib7","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","article-title":"Deep learning for tomato diseases: classification and symptoms visualization","volume":"vol.31","author":"Brahimi","year":"2017","journal-title":"Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.jbiotec.2023.07.011_bib8","doi-asserted-by":"crossref","first-page":"951","DOI":"10.3390\/electronics11060951","article-title":"AlexNet convolutional neural network for disease detection and classification of tomato leaf","volume":"vol.11","author":"Chen","year":"2022","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.jbiotec.2023.07.011_bib9","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TCYB.2014.2322602","article-title":"A competitive swarm optimizer for large scale optimization","volume":"vol.45","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"10.1016\/j.jbiotec.2023.07.011_bib10","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/agriengineering3020020","article-title":"Automatic and reliable leaf disease detection using deep learning techniques","volume":"vol.3","author":"Chowdhury","year":"2021","journal-title":"AgriEngineering"},{"issue":"4","key":"10.1016\/j.jbiotec.2023.07.011_bib11","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1007\/s11554-020-00987-8","article-title":"A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU","volume":"vol.18","author":"Gadekallu","year":"2021","journal-title":"J. Real. -Time Image Process."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib12","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/5219471","article-title":"A benchmarking of learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases","author":"Gutierrez","year":"2019","journal-title":"J. Sens."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib13","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2021.02.009","article-title":"Accelerated gradient algorithm for RBF neural networ","volume":"vol.441","author":"Han","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib14","doi-asserted-by":"crossref","unstructured":"Hlaing, C.S. and Zaw, S.M.M., \"Model-based statistical features for mobile phone image of tomato plant disease classification\", In 2017 18th international conference on parallel and distributed computing, applications and technologies (PDCAT), pp. 223\u2013229, December 2017.","DOI":"10.1109\/PDCAT.2017.00044"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib15","unstructured":"Jayswal, H.S. and Chaudhari, J.P., \"Plant Leaf Disease Detection and Classification using Conventional Machine Learning and Deep Learning\", 2020."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib16","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1109\/CCDC49329.2020.9164457","article-title":"A tomato leaf diseases classification method based on deep learning","author":"Jiang","year":"2020","journal-title":"2020 Chin. Control Decis. Conf. (CCDC)"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib17","doi-asserted-by":"crossref","unstructured":"Kaushik, M., Prakash, P., Ajay, R. and Veni, S., \"Tomato leaf disease detection using convolutional neural network with data augmentation\", In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1125\u20131132, June 2020.","DOI":"10.1109\/ICCES48766.2020.9138030"},{"issue":"10","key":"10.1016\/j.jbiotec.2023.07.011_bib18","doi-asserted-by":"crossref","DOI":"10.3390\/math10101714","article-title":"A novel stacking-based deterministic ensemble model for infectious disease prediction","volume":"vol.10","author":"Mahajan","year":"2022","journal-title":"mathematics"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib19","doi-asserted-by":"crossref","unstructured":"Mokhtar, U., Ali, M.A., Hassenian, A.E. and Hefny, H., \"Tomato leaves diseases detection approach based on support vector machines\", In 2015 11th International computer engineering conference (ICENCO), pp. 246\u2013250, December 2015.","DOI":"10.1109\/ICENCO.2015.7416356"},{"issue":"12","key":"10.1016\/j.jbiotec.2023.07.011_bib20","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.3390\/rs12121920","article-title":"Non-destructive early detection and quantitative severity stage classification of Tomato Chlorosis Virus (ToCV) infection in young tomato plants using vis\u2013NIR Spectroscopy","volume":"vol.12","author":"Morellos","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib21","first-page":"37515","volume":"vol.10","author":"Noroozi","year":"2022","journal-title":"\"Gold. Search Optim. Algorithm\""},{"issue":"1","key":"10.1016\/j.jbiotec.2023.07.011_bib22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"vol.1","author":"Poli","year":"2007","journal-title":"Swarm Intell."},{"issue":"1","key":"10.1016\/j.jbiotec.2023.07.011_bib23","first-page":"19","article-title":"Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems","volume":"vol.7","author":"Rao","year":"2016","journal-title":"Int. J. Ind. Eng. Comput."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib24","doi-asserted-by":"crossref","unstructured":"Rashid, M.H.O., Mamun, M.A., Hossain, M.A. and Uddin, M.P., \"Brain tumor detection using anisotropic filtering, SVM classifier and morphological operation from MR images,\" In proceedings of international conference on computer, communication, chemical, material and electronic engineering, pp. 1\u20134, 2018.","DOI":"10.1109\/IC4ME2.2018.8465613"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib25","unstructured":"Ruder, S., \"An overview of gradient descent optimization algorithms,\" arXiv preprint arXiv:1609.04747, 2016."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib26","doi-asserted-by":"crossref","unstructured":"Sabrol, H. and Satish, K., \"Tomato plant disease classification in digital images using classification tree\", In 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1242\u20131246, April 2016.","DOI":"10.1109\/ICCSP.2016.7754351"},{"issue":"11","key":"10.1016\/j.jbiotec.2023.07.011_bib27","doi-asserted-by":"crossref","first-page":"468","DOI":"10.3390\/plants8110468","article-title":"Plant disease detection and classification by deep learning","volume":"vol.8","author":"Saleem","year":"2019","journal-title":"Plants"},{"issue":"05","key":"10.1016\/j.jbiotec.2023.07.011_bib28","first-page":"1","article-title":"Deep learning convolution neural network to detect and classify tomato plant leaf diseases","volume":"vol.7","author":"Salih","year":"2020","journal-title":"Open Access Libr. J."},{"issue":"1","key":"10.1016\/j.jbiotec.2023.07.011_bib29","article-title":"Development of concise convolutional neural network for tomato plant disease classification based on leaf images","volume":"Vol.1845","author":"Sembiring","year":"2021","journal-title":"J. Phys.: Conf. Ser."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib30","doi-asserted-by":"crossref","unstructured":"Sravan, V., Swaraj, K., Meenakshi, K. and Kora, P., \"A deep learning based crop disease classification using transfer learning\", 2021.","DOI":"10.1016\/j.matpr.2020.10.846"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib31","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/978-981-16-8512-5_29","article-title":"Convolutional neural network-based tomato plant leaf disease detection","author":"Tejashwini","year":"2022","journal-title":"Innov. Electron. Commun. Eng."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib32","unstructured":"Tomato leaf disease detection using CNN dataset <underline>https:\/\/www.kaggle.com\/datasets\/kaustubhb999\/tomatoleaf<\/underline> (Accessed July 2022)."},{"issue":"23","key":"10.1016\/j.jbiotec.2023.07.011_bib33","doi-asserted-by":"crossref","first-page":"7987","DOI":"10.3390\/s21237987","article-title":"Early detection and classification of tomato leaf disease using high-performance deep neural network","volume":"vol.21","author":"Trivedi","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.jbiotec.2023.07.011_bib34","doi-asserted-by":"crossref","unstructured":"Verma, S., Chug, A. and Singh, A.P., \"Prediction models for identification and diagnosis of tomato plant diseases\", In 2018 International Conference on advances in computing, communications and informatics (ICACCI), pp. 1557\u20131563, September 2018.","DOI":"10.1109\/ICACCI.2018.8554842"},{"issue":"3","key":"10.1016\/j.jbiotec.2023.07.011_bib35","article-title":"An intelligent forecasting model for disease prediction using stack ensembling approach","volume":"vol.70","author":"Verma","year":"2022","journal-title":"Comput., Mater. Contin."},{"issue":"1","key":"10.1016\/j.jbiotec.2023.07.011_bib36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1743-422X-10-142","article-title":"Highly sensitive serological methods for detecting tomato yellow leaf curl virus in tomato plants and whiteflies","volume":"vol.10","author":"Xie","year":"2013","journal-title":"Virol. J."},{"key":"10.1016\/j.jbiotec.2023.07.011_bib37","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N. and Liang, J.U., \"A Nested U-Net Architecture for Medical Image Segmentation\", arXiv preprint arXiv:1807.10165, 2018.","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Journal of Biotechnology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168165623001384?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168165623001384?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T13:02:13Z","timestamp":1759150933000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168165623001384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":37,"alternative-id":["S0168165623001384"],"URL":"https:\/\/doi.org\/10.1016\/j.jbiotec.2023.07.011","relation":{},"ISSN":["0168-1656"],"issn-type":[{"value":"0168-1656","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tomato plant leaf disease segmentation and multiclass disease detection using hybrid optimization enabled deep learning","name":"articletitle","label":"Article Title"},{"value":"Journal of Biotechnology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbiotec.2023.07.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}