Resumen
The cover of corn stover has a significant effect on the emergence and growth of soybean seedlings. Detecting corn stover covers is crucial for assessing the extent of no-till farming and determining subsidies for stover return; however, challenges such as complex backgrounds, lighting conditions, and camera angles hinder the detection of corn stover coverage. To address these issues, this study focuses on corn stover and proposes an innovative method with which to extract corn stalks in the field, operating an unmanned aerial vehicle (UAV) platform and a U-Net model. This method combines semantic segmentation principles with image detection techniques to form an encoder?decoder network structure. The model utilizes transfer learning by replacing the encoder with the first five layers of the VGG19 network to extract essential features from stalk images. Additionally, it incorporates a concurrent bilinear attention module (CBAM) convolutional attention mechanism to improve segmentation performance for intricate edges of broken stalks. A U-Net-based semantic segmentation model was constructed specifically for extracting field corn stalks. The study also explores how different data sizes affect stalk segmentation results. Experimental results prove that our algorithm achieves 93.87% accuracy in segmenting and extracting corn stalks from images with complex backgrounds, outperforming U-Net, SegNet, and ResNet models. These findings indicate that our new algorithm effectively segments corn stalks in fields with intricate backgrounds, providing a technical reference for detecting stalk cover in not only corn but also other crops.