Typical Crop Classification of Agricultural Multispectral Remote Sensing Images by Fusing Multi-Attention Mechanism ResNet Networks

Traditional crop classification methods have three critical limitations: (1) dependency on labor-intensive field surveys with limited spatial coverage, (2) susceptibility to human subjectivity during manual data collection, and (3) the inability to capture fine-grained spectral variations due to the lack of multispectral analysis. This research introduces an enhanced crop classification and identification model based on a residual ResNet network. This model leverages multispectral remote sensing images from unmanned aerial vehicles (UAVs) to accurately classify complex crop planting structures. The research focuses on four typical crops: sunflower, corn, beet, and pepper. By acquiring and preprocessing multispectral remote sensing image data, an improved ResNet50 model integrating the ACmix self-attention module and a coordinate attention mechanism is developed to enhance the classification and recognition accuracy of these crops. Experimental results demonstrate that the improved model achieves a classification accuracy of 97.8% on multispectral images, outperforming both RGB images and traditional methods. This research highlights the potential of combining UAV multispectral remote sensing technology with deep learning for precise crop classification, offering valuable technical support for precision agriculture management.