In order to analyze the drought of maize plants and aiming at the difficulty and broad in recognizing agricultural drought, we propose a method to identify the drought of maize plants based on the multi-features fusion. The images of normal and seriously drought plants are taken as samples. The K-means algorithm is used to extract the interesting areas of maize plant images. And, the features of the pictures are extracted after image segmentation, including colors, singular value decomposition (SVD) and textures, a total of 20 dimensional features. The genetic algorithm is used to select a effective features subset of 20 dimensional features. Finally, the discrimination model based on least squares support vector machine is established for the effective features subset and images of maize plant drought are obtained. The single feature (color、SVD、texture) after directly fusion and using principal component analysis for feature selection are performed as comparative experiments, the average recognition accuracies are 0.9503, 0.9627, 0.9771, 0.9460, 0.9745, respectively. The genetic algorithm is used to select the features, and finally finds 9 dimensional features as the optimal solution. The average recognition accuracy is 0.9903. The result shows that this image processing technology can identify the drought situation of the maize plants effectively and efficiently. And it also provides a new idea for the drought identification of maize plants.