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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Nandan, Shiva; Srivastava, Shilpa; Mehndiratta, Vandana; Agarwal, Ritu; Singh, Meenu Title A name given to the resource Multi Disease Identification in Tomato Plant using CNN and SVM Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 5th International Conference on Advancement in Electronics and Communication Engineering, AECE 2025;pp.43-49 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AECE67531.2025.11386585" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AECE67531.2025.11386585</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035171856?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035171856?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nandan S., Christ University, Department of Computer Science, Bengaluru, India; Srivastava S., Christ University, Department of Computer Science, Bengaluru, India; Mehndiratta V., Christ University, Department of Computer Science, Bengaluru, India; Agarwal R., Raj Kumar Goel Institute of Technology, Ghaziabad, India; Singh M., Vsb - Technical University of Ostrava, Ostrava, Czech Republic Description An account of the resource Tomato is a major trade crop; it is among the most widely consumed crops in daily life. Crop diseases reduce not only the quality of the crops but also their amount of production, thus, detection and identification of the specific diseases is of great importance. Diseases like the Mosaic virus, Bacterial Spot, and Yellow Leaf Curl Virus infect the tomato plant. The advanced detection and classification techniques are mainly employed in the diagnosis of these diseases. This helps in informing the farmers about the types of diseases that attack their crops. In this study, independent CNN and SVM classifiers built to classify the diseases. The CNN model extracts feature such as color and leaf edges from input images- then, it proceeds to classification. For SVM, PCA is applied for feature reduction in order to enhance performance and accuracy before classification. A dataset sourced from plant village has been utilized to train the network CNN and SVM. The proposed neural network model has been applied to categorize 4 types of tomato leaf conditions: one healthy and three diseased types of tomato leaves. The results show that the SVM approach achieves a classification accuracy of 94.33%, whereas the CNN model has slightly higher accuracy of 95.17%. 2025 IEEE. Subject The topic of the resource Convolutional Neural Network (CNN); Machine Learning; Support Vector Machine (SVM); Tomato Plant Leaf Disease Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155296-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online