Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture
- Title
- Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture
- Creator
- Asha M.S.; Yogish H.K.
- Description
- The paper explores the application of Convolutional Neural Networks (CNNs), specifically ResNet-18, in revolutionizing the identification of diseases in tomato crops. Facing threats from pathogens like Phytophthora infestans, timely disease detection is crucial for mitigating economic losses and ensuring food security. Traditionally, manual inspection and labour-intensive tests posed limitations, prompting a shift to CNNs for more efficient solutions. The study uses a well-organized dataset, employing data preprocessing techniques and ResNet-18 architecture. The model achieves remarkable results, with a 91% F1 score, indicating its proficiency in distinguishing healthy and unhealthy tomato leaves. Metrics such as accuracy, sensitivity, specificity, and a high AUC score on the ROC curve underscore the model's exceptional performance. The significance of this work lies in its practical applications for early disease detection in agriculture. The ResNet-18 model, with its high precision and specificity, presents a powerful tool for crop management, contributing to sustainable agriculture and global food security. (2024), (Science and Information Organization). All Rights Reserved.
- Source
- International Journal of Advanced Computer Science and Applications, Vol-15, No. 1, pp. 876-883.
- Date
- 2024-01-01
- Publisher
- Science and Information Organization
- Subject
- binary classification; Convolution neural networks; deep learning; disease detection; tomato crop health
- Coverage
- Asha M.S., Department of Computer Science and Engineering, Christ University (Deemed to be University), Karnataka, India; Yogish H.K., Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Karnataka, India, Visvesvaraya Technological University, Belagavi, 590018, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 2158107X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Asha M.S.; Yogish H.K., “Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13742.