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 Sreelatha, Bolisetty; Rajamohanan, Rajasree; Kalaiarasi, G.; Praveen, Rvs; Bugge, Bhagya Prasad; John, Tegil J Title A name given to the resource A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases 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 Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025;pp.1617-1622 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICESC65114.2025.11212443" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICESC65114.2025.11212443</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022632840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022632840?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 Sreelatha B., Geethanjali College of Engineering and Technology, Department of Electronics and Communication Engineering, Telangana, Hyderabad, India; Rajamohanan R., SP Jain School of Global Management, Department of Artificial Intelligence and Data Science, Dubai, United Arab Emirates; Kalaiarasi G., Dhanalakshmi Srinivasan College of Engineering, Department of Food Technology, Coimbatore, India; Praveen R., Director - Utilities Americas LTIMindtree, Houston, TX, United States; Bugge B.P., S.R.K.R Engineering College, Department of Electronics and Communication Engineering, Andhra Pradesh, India; John T.J., Christ University Dharmaram College Hosur, Department of Computer Science, Karnataka, Bengaluru, India Description An account of the resource The early and precise diagnosis of vegetable plant diseases is crucial for sustainable agriculture since these diseases have a major impact on crop output and quality. Disease identification performance is examined in this work through a robust detection pipeline that examines the effects of several preprocessing techniques, class imbalance handling strategies, and deep learning classifiers. To better represent data and increase model knowledge of illness characteristics, the GLCM was used to extract texture information. By combining XGBoost with LSTM networks, a new hybrid model was created. While XGBoost is great at classifying structured data, the LSTM component is great at evaluating sequential data, which allows it to capture patterns and trends in the evolution of plant diseases over time. Better and more meaningful forecasts are made possible by this supplementary integration. By surpassing more conventional methods of illness classification, the suggested LSTM-XG model attained a remarkable prediction accuracy of 99.34%. An important factor in achieving this outcome was the use of hybrid modeling in conjunction with thorough preprocessing and correction of class imbalance. Finally, the LSTM-XG model shows great promise for practical use in precision farming. Its precision and efficiency in identifying illnesses in vegetable plants might facilitate prompt action, lessen crop loss, and encourage better farming methods. 2025 IEEE. Subject The topic of the resource Extreme Gradient Boosting (XGBoost); Gray-Level Co-occurrence Matrix (GLCM); the Long Short-Term Memory (LSTM) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155503-0; 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