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Straw returning is one of the most important measures for increasing fertility. But straw returning has not been widely popularized at present. It needs to be supervised and tested. However, manual detection of straw coverage is time-consuming, laborious, low accuracy and difficult to store information. In order to solve these problems, a straw coverage detection method was proposed based on image segmentation. Considering the precision of traditional image segmentation method was not high, and the computation was complex for multi-threshold segmentation, the search mechanism of gray wolf (GWO) algorithm and differential evolution (DE) algorithm were combined, and a multi-threshold automatic segmentation method was proposed based on image, DE-GWO algorithm for field straw mulching detection. Firstly, the straw mulching image collected in the field was preprocessed, and the adaptive Tsallis entropy was used as the objective function of the algorithm to evaluate the efficiency of image segmentation. Secondly, the number of segmentation thresholds was selected according to the complexity of the image, and the multi-threshold image was segmented by DE-GWO algorithm. The proportion of the images after the segmentation was calculated by the gray degree level. Finally, the straw mulching rate in the image and the actual geographic area were converted according to the shooting height and the wide angle of the camera. The experimental results showed that the straw mulching rate in the field and the actual measurement error were less than 8%, and the DE-GWO algorithm was more accurate than the improved particle swarm optimization (PSO) and gray wolf algorithm (GWO). Compared with manual measurement, the average consumption time was reduced by more than 1500 times. In addition, a set of software system for detection of straw coverage based on DE-GWO algorithm was developed, which provided the basis of algorithm and software support for the real-time detection of the monitoring system.