Research on weed identification in soybean fields based on the lightweight segmentation model DCSAnet

Abstract

Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops.In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability. To improve the weed detection capability of mobile weed control devices, this paper proposes a lightweight weed segmentation network model DCSAnet that can be better applied to mobile weed control devices. The whole network model uses an encoder-decoder structure and the DCA module as the main feature extraction module. The main body of the DCA module is based on the reverse residual structure of MobileNetV3, effectively combines asymmetric convolution and depthwise separable convolution, and uses a channel shuffle strategy to increase the randomness of feature extraction. In the decoding stage, feature fusion utilizes the high-dimensional feature map to guide the aggregation of low-dimensional feature maps to reduce feature loss during fusion and increase the accuracy of the model. To validate the performance of this network model on the weed segmentation task, we collected a soybean field weed dataset containing a large number of weeds and crops and used this dataset to conduct an experimental study of DCSAnet. The results showed that our proposed DCSAnet achieves an MIoU of 85.95% with a model parameter number of 0.57 M and the highest segmentation accuracy in comparison with other lightweight networks, which demonstrates the effectiveness of the model for the weed segmentation task.