High-efficiency real-time rice leaf disease classification using convolutional neural network accelerator on FPGA for edge computing in precision agriculture

Abstract

Convolutional Neural Networks (CNNs) are revolutionizing agriculture, finding widespread applications in tasks such as crop disease detection and yield prediction. However, their high accuracy ability often requires significant computational and memory resources, posing challenges for deployment on resource-constrained edge devices. Lightweight CNNs can reduce computational requirements but still encounter substantial memory access challenges, necessitating further optimization for practical edge deployment. Therefore, this study employed MobileNetV2 for rice leaf disease classification and adapt the model for edge computing by quantizing network parameters to 16-bit for Field-Programmable Gate Array (FPGA) storage, implementing a linear buffering method to reduce parameter read operations, thus alleviating communication bandwidth bottlenecks and improving memory utilization by 28.6 %. High Level Synthesis (HLS) tools were also applied to optimize the FPGA accelerator through loop unrolling, pipelining, and matrix partitioning, enhancing data parallelism and reuse. The optimized design was deployed on a ZYNQ-AC7Z020 FPGA platform, achieving 95.8 % classification accuracy, an inference speed of 53 ms per frame, power consumption of 3.09 W, and a throughput of 35.7 GOPS (Giga Operations Per Second). Memory usage was reduced by 47.1 % without compromising performance. This cost-effective and efficient design offers a robust solution for real-time rice leaf disease classification, balancing resource constraints and operational performance for edge applications in precision agriculture.