Abstract: 【Objective】The purpose of this paper is to explore the effect of identifying maize drought, for which the color and texture features of maize and the neural network modeling method were used. 【Method】The maize images under different drought stress were collected by visible light imaging method, from which the color and texture features of maize were extracted automatically by programming. Then the drought identification models of different maize growth stages were built by the ensemble learning of multiple BP neural networks. 【Result】The results show that both the average identification accuracy and the average identification precision of the maize emergence-jointing stage model are more than 90% at training and validation, and the average value of training and validation errors are less than 0.1. The average identification accuracy and the average identification precision of the jointing-tasseling stage model are more than 85% at the training and validation, and the average identification errors in training and validation are 0.1 and 0.14. The average drought identification accuracies of the tasseling-maturing stage model at training and validation are 85.36% and 84.27%, and the average precision is more than 80%, and the average identification errors are 0.15 and 0.16. All the maize growth stage drought identification models can identify field maize drought with the about 75% average accuracy and the more than 80% average identification precision, and the average identification error values of three growth stage models are 0.22, 0.21, 0.29. 【Conclusion】In conclusion, the ability of identifying drought of maize emergence-jointing stage model is strongest, followed by the jointing-tasseling stage drought identification model, but the drought identification model for tasseling-maturing stage is relatively poor. For the same drought level identification using the same model, the identification effect at training is best, second at validation, but relatively poor at testing. For the different drought level identifications using the same model, the identification effect for medium drought is not ideal, but better for drought-free and extreme level. The results provide a reference for the maize drought pattern recognition and the automatic monitoring, and lay the foundation for the early warning of maize drought by using phenotypic characteristics.