At present, the accuracy of automatic identification of straw coverage rate is low, an image processing algorithm was proposed, which based on the combination of straw image distortion correction and Otsu algorithm for threshold segmentation. It was used to calculate the straw coverage rate in the field. Firstly, the working environment information of the no-tillage planter was collected by monocular camera, and an improved AdaBoost algorithm was used to automatically judge whether the current working environment of no-tillage planter was no-tillage land. Under the premise of no-tillage land, an improved AdaBoost algorithm was proposed to determine the working environment of no-tillage planter. Secondly, the straw image collected in the field was preprocessed, and the recognizable features of straw in the image were improved by color space distance and image enhancement. The inverse mapping model was combined with nearest neighbor interpolation to solve the problem of image distortion. Finally, the image part for straw recognition was cut out. The Otsu algorithm was used for threshold segmentation to calculate the straw coverage rate. The accuracy of AdaBoost algorithm classification and straw coverage rate was verified by experiments. The experimental results showed that the working environment of no-tillage planter was effectively indentified by AdaBoost algorithm，and the error of straw coverage rate between the image processing algorithm calculated and the actual measurement value was less than 5%, which verified the effectiveness of the algorithm.