Abstract: Cotton is one of the most important economic crops in China. It is of great significance to predict the yield of cotton. In this study, the Unmanned Aerial Vehicle (UAV) remote sensing platform was first used to collect some data on the densely planted cotton in Xinjiang, China. Five-point sampling was also utilized in the period of defoliation. A total of five cotton fields were then selected as well. Secondly, each image was divided into multiple sub-images and then enhanced by color space conversion, brightness, noise blurring, flipping and rotating operation. A high-quality dataset was constructed to estimate the cotton yield. Better training was achieved, compared with the original. Eventually, the cotton dataset with the construction site was used to extract the element rate from the cotton images using RCH-UNet (resnet coordinate hardswish UNet). Among them, the UNet was used as a baseline model to construct an improved U-type convolutional neural network. While the IoU (intersection over union), PA (pixel accuracy), and precision were taken as the evaluation metrics. The overall RCH-UNet improved the three metrics by 14.34, 9.85, and 8.68 percentage points, respectively, compared with the original UNet. Specifically, ResNet50 backbone feature extraction network was selected to replace the traditional CBR (convolution batch normalization ReLU) downs sampling structure in UNet; The CA (coordinate attention) mechanism was combined with the UNet; The ReLU activation function in UNet was replaced with the hardswish activation function. The results showed that: 1) The richer semantic information in the ResNet50 was learned through the residual structure, indicating the better feature extraction and expression of the improved model. 2) The CA mechanism was significantly enhanced to learn the detailed features. At the same time, there was an effective reduction in the interference of irrelevant features to the model. 3) The hardswish activation function presented the stronger expression and feature fusion of UNet when performing up-sampling and jump connection. Subsequently, the performance of RCH-UNet was tested with the PSPNet and DeepLabv3 models under the same experimental conditions. The IoU, PA, and precision of RCH-UNet had improved by 9.15, 6.31, and 3.99 percentage points, respectively, compared with the DeepLabv3 model. A prediction model of cotton yield was constructed with the ridge regression from the cotton pixels extracted by the RCH-UNet model. The image texture features were also extracted by GLCM (gray-level co-occurrence matrix). The R² value of the improved model was 0.92, and the average relative error between the predicted and actual yield was 9.254%. The RCH-UNet model was accurately and effectively extracted from the cotton images; Meanwhile, the prediction model of cotton yield was effectively verified using deep learning and image processing. The UAV low-altitude imaging can also be expected to predict the yield of densely planted cotton in Xinjiang , China.