Research on agricultural remote sensing image object detection method based on neural network optimization method

In the context of the rapid development of modern agriculture, agricultural remote sensing image object detection plays a crucial role in crop monitoring, yield estimation, and agricultural resource management. This paper focuses on the research of object detection methods for agricultural remote sensing images based on neural network optimization. First, it analyzes the characteristics and challenges of agricultural remote sensing images. Then, it elaborates on the basic principles of neural network - based object detection algorithms, such as YOLO (You Only Look Once). Aiming at the problems of low detection accuracy and high false alarm rate in traditional methods, this paper proposes an optimization strategy. The proposed method improves the network structure by introducing attention mechanisms and multi - scale feature fusion techniques. Experimental results show that the optimized neural network method significantly improves the accuracy and efficiency of object detection in agricultural remote sensing images, providing a more reliable technical means for agricultural production and management.