Chlorophyll is an important indicator reflecting the nitrogen nutrition status of crops, and its content is closely related to crop growth and development, photosynthesis capacity and crop yield. With the increasing maturity of image processing technology, choosing image color features to estimate the chlorophyll content of crops has become an important technical means. Taking the wheat canopy image in the natural environment as the research object, a color feature selection method was proposed based on the entropy weight method, and machine learning methods were applied to establish a wheat canopy chlorophyll content estimation model. The entropy method used information entropy to measure the weight of color feature indicators to achieve the canopy image feature ranking. The machine learning method used multiple linear regression (MLR), ridge regression (RR) and support vector regression models (SVR) to estimate the chlorophyll content of wheat canopy. The experimental results showed that compared with the feature set selected by the Pearson correlation coefficient method and principal component analysis, the entropy weight method obtained a*, R-B-G, R-G, (a*+b*)/L, a*/b*, (R-G)/(R +G+B), (R-B)/(R+B), H/S, (R-G)/(R+G) and other nine features. The feature sets can use fewer feature indicators to achieve the best prediction effect. In the case of selecting the same characteristic index parameters, the predictive ability of SVR was better than that of other models, and the average values of R2 and RMSE were 0.80 and 1.89,compared with MLR and RR models, its R2was improved by 2.8% and 1.1%, RMSE was decreased by 0.13 and 0.05,respectively.The SVR model based on the entropy weight method was applied to the wheat canopy image data collected in 2021, and the results showed that the model had good stability. The above research results showed that image processing technology and machine learning methods had very good application value in the estimation of chlorophyll content of crops, providing an important theoretical basis for imagebased estimation of chlorophyll content of field crops.