Effective Edge Solution for Early Detection of Rice Disease on ARM- M Microcontroller

The increasing need for efficient and sustainable agriculture has driven the adoption of advanced technologies like Artificial Intelligence (AI) in crop management. This study evaluates the feasibility of deploying AI models on ARM-M microcontrollers for real-time detection of rice plant diseases, crucial for early intervention and crop health management. We developed and assessed the performance of five Convolutional Neural Network (CNN) architectures: MobileNet V1, MobileNet V2, FD-MobileNet, ResNet, and SqueezeNet. These models were trained on a dataset comprising 5932 high-quality images of rice leaves affected by four prevalent diseases: bacterial blight, blast, brown spot, and tungro. The AI models were optimized and quantized to meet the computational and memory constraints of the ARM-M microcontroller. Our evaluation focused on accuracy, computational efficiency, and resource utilization. The results demonstrated that FD-MobileNet achieves high accuracy of 98.44% with lower computational costs, making it suitable for deployment in resource-constrained environments. The deployment on ARM-M microcontrollers showcased the potential for real-time, on-site disease monitoring, enabling farmers to take timely and effective actions. This study underscores the transformative impact of integrating AI with embedded systems in smart agriculture, promoting sustainable farming practices and enhancing food security.