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Title A name given to the resource A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation Subject The topic of the resource Image segmentation; Leaf disease; Machine learning; Particle swarm optimization and edge feature detection Description An account of the resource Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves. 2024 The Authors Creator An entity primarily responsible for making the resource Rajagopal M.; Kayikci S.; Abbas M.; Sivasakthivel R. Source A related resource from which the described resource is derived Heliyon, Vol-10, No. 12 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.heliyon.2024.e32707" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.heliyon.2024.e32707</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196307387&amp;doi=10.1016%2Fj.heliyon.2024.e32707&amp;partnerID=40&amp;md5=a31af7d764bfbfe9d8c0fcb35ce7cdab" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196307387&amp;doi=10.1016%2fj.heliyon.2024.e32707&amp;partnerID=40&amp;md5=a31af7d764bfbfe9d8c0fcb35ce7cdab</a> Rights Information about rights held in and over the resource All Open Access; Green Open Access Relation A related resource ISSN: 24058440 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajagopal M., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India; Kayikci S., Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey; Abbas M., Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; Sivasakthivel R., Department of Computer Science, School of Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India