Lightweight Model for Rice Crop Disease Detection

Rice is a staple crop for more than half of the world's population, making its health and yield crucial for global food security. Early detection of rice crop diseases can significantly mitigate yield losses and reduce the use of chemical treatments. In this paper, we propose a lightweight deep learning model designed for the efficient detection of rice crop diseases. The model leverages convolutional neural networks (CNNs) optimized for mobile and edge devices, ensuring that farmers in remote or resource-constrained areas can benefit from real-time disease detection. We demonstrate that our model achieves competitive accuracy with significantly reduced computational complexity compared to traditional models, making it an ideal solution for on- field deployment.