Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA).The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification.Currently, crop identification relies heavily on complex high-precision models that often struggle to provide real-time performance.Research on lightweight models specifically for crop classification is also limited.In this paper, we propose a crop classification method based on UAV visible-light images based on PP-LiteSeg, a lightweight model proposed by Baidu.
To improve the accuracy, a pyramid pooling module is designed in this paper, which integrates adaptive mean pooling and CSPC (Convolutional Spatial Pyramid Pooling) techniques to handle high-resolution features.In addition, a sparse self-attention mechanism is employed to help Unite Warriors the model pay more attention to locally important semantic regions in the image.The combination of adaptive average pooling and the sparse self-attention mechanism can better handle different levels of contextual information.To train the model, a new dataset based on UAV visible-light images including nine categories such as rice, soybean, red bean, wheat, corn, poplar, etc., with a time span of two years was created for accurate crop classification.
The experimental results show that the improved model outperforms other models in terms of accuracy and prediction performance, with a MIoU (mean intersection ratio joint) of 94.79%, which is 2.79% better than the original model.Based on the UAV RGB Fender Flares images demonstrated in this paper, the improved model achieves a better balance between real-time performance and accuracy.In conclusion, the method effectively utilizes UAV RGB data and lightweight deep semantic segmentation models to provide valuable insights for crop classification and UAV field monitoring.