A LIGHTWEIGHT IMPROVED YOLOv5s MODEL-BASED RICE BLAST DETECTION METHOD AND MOBILE DEPLOYMENT

For achieving more efficient recognition results and deployment on mobile devices, a rice blast recognition model was constructed by making lightweight improvements to YOLOv5s. First, using YOLOv5s as the base, GhostConv was introduced to replace standard convolution in its backbone and neck, and LightC3 module was built to improve the C3 module in the neck. This significantly reduced the computational burden and model size. Furthermore, Concat operator was replaced with BiFPN and SE attention mechanism was integrated to maintain accuracy when reducing model complexity. These modifications enhanced the model's ability to capture fine-grained features and multi-scale information. The experimental results showed that the designed model had a 49% decrease in the number of model parameters and a 50% decrease in FLOPs without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s, achieving the good balance between detection performance and model lightweight. Then, an APP named RiceBlastDetector was built based on the model, achieving accurate detection in the scenario with the different characterization scale disease spots from experiments in the field, which can provide a reference for detecting other crop diseases.