基于RCH-UNet的新疆密植棉花图像快速分割及产量预测

Prediction of cotton yield densely planted in Xinjiang of China using RCH-UNet model

针对复杂棉田环境下传统图像分割技术存在分割精度低、实时性弱和鲁棒性差等问题,该研究以脱叶期新疆密植棉花为对象,结合低空无人机遥感平台,提出一种RCH-UNet(resnet coordinate hardswish UNet)棉花产量快速预测模型。将UNet中传统的CBR(convolution batch normalization ReLU)下采样模块替换为ResNet50,同时将CA(coordinate attention)注意力机制和hardswish激活函数引入UNet,以提高图像特征的提取能力,增强图像分割效果。基于无人机采集的棉花图像数据集评估RCH-UNet模型性能。试验结果表明,在该研究构建的棉花图像数据集上,RCH-UNet模型的棉花分割交并比达到92.79%,像素准确率达到96.22%,精确率为96.30%,与原始U-Net、PSPNet和DeepLabv3相比,像素准确率分别提高了9.85、17.67、6.31个百分点。通过RCH-UNet提取棉花像素比和灰度共生矩阵提取纹理特征,结合岭回归分析构建多因素棉花产量预测模型,模型的R^(2)为0.92,预测产量与实际产量平均绝对百分比误差为9.254%。研究结果可为新疆密植棉花产量预测提供技术支持。

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 R^(2) 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.