{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T01:21:16Z","timestamp":1783041676186,"version":"3.54.6"},"reference-count":12,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks (CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model\u2019s weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model\u2019s performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models.<\/jats:p>","DOI":"10.3233\/jifs-232067","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T13:40:15Z","timestamp":1702042815000},"page":"2483-2494","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced symbiotic organism search optimization algorithm for plant disease classification"],"prefix":"10.1177","volume":"46","author":[{"given":"Priyanga","family":"Subbiah","sequence":"first","affiliation":[{"name":"Department of Networking and Communcations, Faculty of Engineering and Technology, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu - 603203. India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnaraj","family":"Nagappan","sequence":"additional","affiliation":[{"name":"Department of Networking and Communcations, Faculty of Engineering and Technology, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu - 603203. India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232067_ref1","doi-asserted-by":"crossref","first-page":"103615","DOI":"10.1016\/j.micpro.2020.103615","article-title":"Performance of deep learning vs machine learning in plant leaf disease detection","volume":"80","author":"Sujatha","year":"2021","journal-title":"Microprocessors and Microsystems"},{"key":"10.3233\/JIFS-232067_ref3","doi-asserted-by":"crossref","first-page":"106279","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas Amreen","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-232067_ref4","doi-asserted-by":"crossref","first-page":"106379","DOI":"10.1016\/j.compag.2021.106379","article-title":"MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks","volume":"189","author":"Sun Henan","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-232067_ref5","doi-asserted-by":"crossref","first-page":"167753","DOI":"10.1016\/j.ijleo.2021.167753","article-title":"Detection of oil palm leaf disease based on color histogram and supervised classifier","volume":"245","author":"Hamdani Hamdani","year":"2021","journal-title":"Optik"},{"key":"10.3233\/JIFS-232067_ref8","doi-asserted-by":"crossref","unstructured":"Venugopal D. , Jayasankar T. , Krishnaraj N. , Venkatraman S. , Prakash N.B. and Hemalakshmi G.R. , and , Multifactorial disease detection usingregressive multi-array deep neural classifier, IntelligentAutomation & Soft Computing 28(1) (2021).","DOI":"10.32604\/iasc.2021.015205"},{"key":"10.3233\/JIFS-232067_ref9","doi-asserted-by":"crossref","unstructured":"Nawaz , Muhammad Amir , Rana Mudassar Rasool , Maryam Kausar , Amir Usman , Tanvir Fatima Naik Bukht , Rizwan Ahmad , Ahmad Jaleel , \u201cPlant disease detection using internet of thing (IoT), International Journal of Advanced Computer Science and Applications 11(1) (2020).","DOI":"10.14569\/IJACSA.2020.0110162"},{"issue":"10","key":"10.3233\/JIFS-232067_ref12","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.3390\/plants9101319","article-title":"Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers","volume":"9","author":"Saleem","year":"2020","journal-title":"Plants (Basel)"},{"issue":"14","key":"10.3233\/JIFS-232067_ref13","doi-asserted-by":"crossref","first-page":"5792","DOI":"10.3390\/su12145792","article-title":"Effects of Circular Economy Policies on the Environment andSustainable Growth: Worldwide Research","volume":"12","author":"Abad-Segura","year":"2020","journal-title":"Sustainability"},{"key":"10.3233\/JIFS-232067_ref14","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s11277-023-10333-3","article-title":"Performance Analysis of Rice Plant Diseases Identification and Classification Methodology","volume":"130","author":"Tholkapiyan","year":"2023","journal-title":"Wireless Pers Commun"},{"key":"10.3233\/JIFS-232067_ref16","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11760-021-01909-2","article-title":"PlantDiseaseNet:convolutional neural network ensemble for plant disease and pestdetection","volume":"16","author":"Turkoglu","year":"2022","journal-title":"SIViP"},{"key":"10.3233\/JIFS-232067_ref17","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s10661-022-10656-x","article-title":"Deep learning system for paddy plant disease detection and classification","volume":"195","author":"Haridasan","year":"2023","journal-title":"Environ Monit Assess"},{"issue":"Part 3","key":"10.3233\/JIFS-232067_ref19","first-page":"3500","article-title":"Image-based Plant Diseases Detection using Deep Learning","volume":"80","author":"Adesh Panchal","year":"2023","journal-title":"Materials Today: Proceedings"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:55Z","timestamp":1777455775000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":12,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232067","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}