A Convolutional Neural Network Approach for Rice Leaf Disease Detection in India Using Deep Learning
DOI:
https://doi.org/10.52783/jns.v14.1909Keywords:
CNN, Rice, Disease, Deep learning, India.Abstract
Rice is a staple food crop in India, and its production is critical for food security. However, rice crops are susceptible to various diseases that can significantly reduce yield and quality. Early detection of these diseases is essential for effective management and mitigation. This paper proposes a Convolutional Neural Network (CNN)-based approach for the automated detection of rice leaf diseases using deep learning. The model is trained on a dataset of rice leaf images collected from different regions of India, encompassing healthy leaves and those affected by common diseases such as blast, brown spot, and bacterial leaf blight. The proposed CNN architecture achieves high accuracy in disease classification, demonstrating its potential as a tool for early disease detection in rice farming. The results highlight the effectiveness of deep learning in agricultural applications, particularly in resource-constrained settings like India.
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