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 Sivasankari, K.; Kousalya, R. Title A name given to the resource Empowering Agriculture using New Approach of Machine Learning Techniques to Detect Early 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 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.11385-11393 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105034870423?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034870423?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 Sivasankari K., Department of Computer Science, Dr. N.G.P Arts and Science College, Bharathiyar University, Tamilnadu, Coimbatore, India; Kousalya R., Dr. N. G. P Arts and Science College Coimbatore, Associate Professor in Computer Science, Christ University, Bangalore, India Description An account of the resource Plant health plays a critical role in ensuring global food security and sustaining agricultural productivity, as it directly influences crop yields and economic stability. Reducing losses and enhancing farm management techniques depend on early plant disease detection. This research suggests a new hybrid framework that combines deep learning (DL) and machine learning (ML) to improve disease detection's precision and effectiveness. The ML component effectively processes structured data, providing clear and reliable recommendations, while the DL model focuses on extracting detailed features from high resolution plant images through advanced image processing. By combining these complementary techniques, the framework achieves high precision, scalability, and real-time disease monitoring capabilities. This innovation supports farmers and agricultural experts in making timely, informed decisions, reducing crop losses and advancing sustainable farming practices. Ultimately, better precision agriculture is made possible by the integration of these cutting edge technology, which supports sustainable agricultural development and global food security. Grenze Scientific Society, 2025. Subject The topic of the resource Deep Learning; EfficientNet-B7 Algorithm; Global Food Security; Hybrid Framework; Machine Learning; XGBOOST Algorithm Publisher An entity responsible for making the resource available Grenze Scientific Society 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