Lightweight and Efficient Wheat Spike Detection for Small Targets

Wheat spike detection is a crucial component of wheat yield prediction. In this study, n lightweight and efficient wheat spike detection model is proposed. The model employs a novel Wheat Spike Net Block (WSNB) within a lightweight network architecture, integrating Depth-Wise Convolution (DW-Conv) and Efficient Window Multi-Head Self-Attention (EW-MHSA) to rapidly process images and accurately identify wheat spikes, even under compact small target conditions. The model is equipped with four detection heads to effectively handle targets of varying scales and incorporates the innovative EMF-IOU loss function for refined bounding box estimation. Tested on a self-constructed Shangluo winter wheat dataset, the model achieves a detection speed of 96.1 FPS on NVIDIA Tesla V100 and mAP@0.5 of 95.3%, surpassing YOLOv5, EfficientV2, YOlOX,transformer, and MobileVIt3 in terms of accuracy and efficiency. The model's performance across diverse hardware platforms highlights its potential for practical implementation in real-time wheat yield estimation and precision agriculture.