{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T01:35:42Z","timestamp":1777685742666,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["HIS"],"published-print":{"date-parts":[[2024,9,19]]},"abstract":"<jats:p>The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection\u2019s severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures.<\/jats:p>","DOI":"10.3233\/his-240031","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T12:31:57Z","timestamp":1721133117000},"page":"207-221","source":"Crossref","is-referenced-by-count":1,"title":["A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm"],"prefix":"10.1177","volume":"20","author":[{"given":"Priyanga","family":"Subbiah","sequence":"first","affiliation":[{"name":"Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit Kumar","family":"Tyagi","sequence":"additional","affiliation":[{"name":"Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishnaraj","family":"N","sequence":"additional","affiliation":[{"name":"Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"41\u201342","key":"10.3233\/HIS-240031_ref1","doi-asserted-by":"publisher","first-page":"31497","DOI":"10.1007\/s11042-020-09669-w","article-title":"Identifying plant diseases using deep transfer learning and enhanced lightweight network","volume":"79","author":"Chen","year":"2020","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/HIS-240031_ref2","doi-asserted-by":"publisher","first-page":"107901","DOI":"10.1016\/jasoc.2021.107901","article-title":"Identifying crop diseases using attention embedded MobileNet-V2 model","volume":"113","author":"Chen","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.3233\/HIS-240031_ref3","doi-asserted-by":"publisher","first-page":"120381","DOI":"10.1016\/jeswa.2023.120381","article-title":"Tomato plant disease classification using Multilevel Feature Fusion with adaptive channel spatial and pixel attention mechanism","volume":"228","author":"CK","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/HIS-240031_ref4","doi-asserted-by":"publisher","first-page":"106089","DOI":"10.1016\/jcompag.2021.106089","article-title":"Development of a high-throughput plant disease symptom severity assessment tool using machine learning image analysis and integrated geolocation","volume":"184","author":"Clohessy","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"issue":"12","key":"10.3233\/HIS-240031_ref5","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.3390\/agronomy11122388","article-title":"Plant disease identification using shallow convolutional neural network","volume":"11","author":"Hassan","year":"2021","journal-title":"Agronomy"},{"issue":"12","key":"10.3233\/HIS-240031_ref6","doi-asserted-by":"publisher","first-page":"9471","DOI":"10.1007\/s00521-021-06388-7","article-title":"Multi-feature decision fusion algorithm for disease detection on crop surface based on machine vision","volume":"34","author":"Hua","year":"2022","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/HIS-240031_ref7","doi-asserted-by":"publisher","first-page":"105342","DOI":"10.1016\/jcompag.2020.105342","article-title":"SoyNet: Soybean leaf diseases classification","author":"Karlekar","year":"2020","journal-title":"Computers and Electronics in Agriculture172"},{"key":"10.3233\/HIS-240031_ref8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/IIPHDW2018.8388338","article-title":"Data augmentation for improving deep learning in image classification problem","author":"Mikolajczyk","year":"2018","journal-title":"2018 International Interdisciplinary PhD Workshop (IIPhDW)"},{"key":"10.3233\/HIS-240031_ref9","doi-asserted-by":"publisher","first-page":"012023","DOI":"10.1088\/1742-6596\/1019\/1\/012023","article-title":"binarization of document images: A comprehensive review","volume":"1019","author":"Mustafa","year":"2018","journal-title":"Journal of Physics: Conference Series"},{"issue":"3","key":"10.3233\/HIS-240031_ref10","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s13198-020-00972-1","article-title":"Systematic review of deep learning techniques in plant disease detection","volume":"11","author":"Nagaraju","year":"2020","journal-title":"International Journal of System Assurance Engineering and Management"},{"key":"10.3233\/HIS-240031_ref11","doi-asserted-by":"publisher","first-page":"105093","DOI":"10.1016\/jcompag.2019.105093","article-title":"Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions","volume":"167","author":"Picon","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/HIS-240031_ref12","doi-asserted-by":"publisher","DOI":"10.36909\/jer11941"},{"key":"10.3233\/HIS-240031_ref13","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/B978-0-12-819764-600010-7","article-title":"Crop disease classification using deep learning approach: an overview and a case study","author":"Rangarajan Aravind","year":"2020","journal-title":"Deep Learning for Data Analytics"},{"key":"10.3233\/HIS-240031_ref14","doi-asserted-by":"publisher","first-page":"107164","DOI":"10.1016\/jasoc.2021.107164","article-title":"Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification","volume":"103","author":"Saeed","year":"2021","journal-title":"Applied Soft Computing"},{"issue":"1","key":"10.3233\/HIS-240031_ref15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"Journal of Big Data"},{"key":"10.3233\/HIS-240031_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/jmatpr.2020.10.846"},{"key":"10.3233\/HIS-240031_ref17","doi-asserted-by":"publisher","first-page":"106468","DOI":"10.1016\/jcompag.2021.106468","article-title":"T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases","volume":"190","author":"Wang","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"issue":"7","key":"10.3233\/HIS-240031_ref18","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1049\/iet-cvi2019.0136","article-title":"Identification of crop diseases using improved convolutional neural networks","volume":"14","author":"Wang","year":"2020","journal-title":"IET Computer Vision"},{"key":"10.3233\/HIS-240031_ref19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5511676","article-title":"Image recognition of crop diseases and insect pests based on deep learning","author":"Xin","year":"2021","journal-title":"Wireless Communications and Mobile Computing"},{"key":"10.3233\/HIS-240031_ref20","doi-asserted-by":"crossref","unstructured":"Subbiah, Priyanga, Nagappan, Krishnaraj, Enhanced symbiotic organism search optimization algorithm for plant disease classification, Journal of Intelligent and Fuzzy Systems (2024), 2483\u20132494.","DOI":"10.3233\/JIFS-232067"},{"key":"10.3233\/HIS-240031_ref21","first-page":"296","article-title":"Automated plant disease detection systems for the smart farming sector","author":"Subbiah","year":"2024","journal-title":"Agriculture and Aquaculture Applications of Biosensors and Bioelectronics"}],"container-title":["International Journal of Hybrid Intelligent Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/HIS-240031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:53:04Z","timestamp":1777452784000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/HIS-240031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":21,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/his-240031","relation":{},"ISSN":["1448-5869","1875-8819"],"issn-type":[{"value":"1448-5869","type":"print"},{"value":"1875-8819","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]}}}