Detection of maize leaf diseases using improved MobileNet V3-small

In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases.