Application of SAR Target Recognition Based on Electromagnetic Scattering Feature Fusion in soybean Detection

In the context of the rapid development of agricultural intelligence, the use of target recognition technology to detect grain phenomena can effectively ensure food safety. Synthetic aperture radar (SAR) technology has been widely used in the field of target recognition due to its high resolution and good anti - jamming ability. In this study, a SAR target recognition method based on electromagnetic scattering features is proposed. This method fuses the deep-learning algorithm YOLOv5 with electromagnetic scattering features to achieve target recognition and classification, and applies it to soybean classification and detection, aiming to provide an efficient and reliable solution for grain detection. Through theoretical analysis and experimental verification, this study demonstrates that the proposed method achieves a high maximum accuracy of 95.7% in identifying complex targets such as soybeans, indicating its excellent performance.