Empowering Agriculture using New Approach of Machine Learning Techniques to Detect Early Plant Diseases
- Title
- Empowering Agriculture using New Approach of Machine Learning Techniques to Detect Early Plant Diseases
- Creator
- Sivasankari, K.; Kousalya, R.
- Description
- 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.
- Source
- 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.11385-11393
- Date
- 01-01-2025
- Publisher
- Grenze Scientific Society
- Subject
- Deep Learning; EfficientNet-B7 Algorithm; Global Food Security; Hybrid Framework; Machine Learning; XGBOOST Algorithm
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sivasankari, K.; Kousalya, R., “Empowering Agriculture using New Approach of Machine Learning Techniques to Detect Early Plant Diseases,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26245.