Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhanced radial basis function neural network for tomato plant disease leaf image segmentation Subject The topic of the resource Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease Description An account of the resource Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V. Creator An entity primarily responsible for making the resource Patil M.A.; M M. Source A related resource from which the described resource is derived Ecological Informatics, Vol-70 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ecoinf.2022.101752" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecoinf.2022.101752</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135888868&amp;doi=10.1016%2Fj.ecoinf.2022.101752&amp;partnerID=40&amp;md5=fae568c73ea3f425f0a62d0a0e3c1e1b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135888868&amp;doi=10.1016%2fj.ecoinf.2022.101752&amp;partnerID=40&amp;md5=fae568c73ea3f425f0a62d0a0e3c1e1b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15749541 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 Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India