Abstract: Taking the soybean leaves chlorophyll as the research object，the chlorophyll SPAD estimation model was constructed based on RGB color space. In this experiment，the prediction for chlorophyll content in soybean leaves was carried out. Firstly，the image of soybean leaves in the natural environment was collected，the image noise was removed by median filtering，and the leaves were segmented from the background based on the kmeans algorithm. Then，the red（R），green（G），and blue（ B）values were extracted from the leaf images，the color feature parameters were constructed by operation combination，the chlorophyll content estimation model based on the color characteristic parameters of soybean leaves was established，and the accuracy was evaluated and verified. Finally，four color characteristic parameters such as R/G/B，R/（R+G-B），B/（R+G-B）and G/（R+GB） were combined， and the regression analysis was carried out on these four color characteristic parameters and chlorophyll measured values. The verification results showed that the RGB color space had the highest combination accuracy，R² was 0. 438，AARD was 9. 58%，and RMSE was 2. 862. The method can predict the chlorophyll content in soybean leaves quickly and non-destructively，and it provides a scientific basis for assessing the physiological state of soybean.

Key words: soybean leaf, color parameter, chlorophyll, RGB model