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 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/. Creator An entity primarily responsible for making the resource Paramesha, K.; Jalapur, Shruti; Hanok, Shalini; Puttegowda, Kiran; Manjunatha, G.; Kumara, Bharath Title A name given to the resource Machine Learning and Deep Learning Approaches for Guava Disease Detection 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 SN Computer Science;Volume;6;Issue;4;Article No.;361; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-025-03886-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-025-03886-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002987447?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002987447?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 Paramesha K., Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, India; Jalapur S., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Hanok S., Department of Electronics and Communication Engineering, ATME College of Engineering, Karnataka, Mysuru, India; Puttegowda K., Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, India; Manjunatha G., Department of CSD, PES Institute of Technology and Management, Karnataka, Shivamogga, India; Kumara B., Department of ECE, MS Ramaiah University of Applied Science (MSRUAS), Karnataka, Bangalore, India Description An account of the resource A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Accuracy; Deep learning; Guava disease; Leaf images; Machine learning Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 2662995X; Language A language of the resource English Type The nature or genre of the resource Article 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