CROP DISEASE DETECTION USING CNN

CROP DISEASE DETECTION USING CNN

B .Vamshi, B. Vaishnavi, Prof. Mohan, B. Thrilok Reddy, B. Surya

Malla Reddy University

Hyderabad

Abstract : Crop diseases are a major challenge in agriculture, causing significant yield losses and threatening global food security. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown great promise in detecting crop diseases. we propose a deep learning approach for crop disease detection using CNNs. Our method is trained on a large dataset of labeled images of healthy and diseased crops and optimized using backpropagation and stochastic gradient descent. We evaluate our approach on several crop types and disease categories and compare its performance with traditional methods. Our experimental results demonstrate that the proposed approach achieves high accuracy and outperforms traditional methods in detecting crop diseases. Moreover, the approach is shown to be robust and effective across different crop types and disease categories, indicating its potential for real-world applications. Overall, our proposed approach provides an effective and efficient solution for crop disease detection, which can help farmers make timely decisions and take necessary actions to prevent yield losses and ensure food security.