Straw coverage rate is one of the most important indicators for conservation tillage evaluation. It is also important to realize online detection of straw coverage rate for black land conservation evaluation. Aiming at the problems of various forms of cropland straws and the difficulty in accurately identifying the broken straws, an online detection algorithm of straw coverage rate was proposed based on the combination of K-means clustering and zoning optimization method with machine vision technology. Firstly, K-means clustering algorithm was used for maize straw image segmentation from the background image. And then the straw image was segmented into 16 areas, using statistical methods to calculate respectively the median of straw and the average number of gray levels, respectively. After 16 area average straw center gray value and soil background gray value were calculated, both were taken as a new classification center. Subsequently the K-means clustering method was used to segment the image of corn straw again. When the gray value of the center of straw did not change, the iteration was stopped. With the calculated number of pixel points of straw, the coverage rate of corn straw was obtained, finally. In April 2021, the proposed algorithm was verified at 100 sampling points in corn fields in Changchun City, Jilin Province. The correlation coefficients between the proposed algorithm and the artificial rope pulling method and the artificial image labeling method were 0.7161 and 0.9068, respectively. The corn straw coverage detection misjudgment rate was 7%. The average error of Otsu thresholding method and classical K-means clustering method was respectively reduced by 45.6% and 29.2%. The experimental results showed that the proposed method could detect the straw coverage rate under different weather conditions and planting patterns, accurately. It can provide a method for online detection of straw coverage.