Research on recognition for cotton spider mites’ damage level based on deep learning

The changes in cotton leaf characteristics are closely related to the cotton spider mites’ damage level. Extracting the distinguishable features of cotton leaves is an effective method to identify the level. However, it faces enormous challenges for the classification due to various factors, such as illumination intensity, background complexity, shooting angle and so on. A recognition model is proposed, which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1. The experiments demonstrate that the deep learning model attains the accuracy of 92.29% for the training set and 91.88% for the test set of the mixed data. For testifying the effectiveness of the two-stage training method, the models are trained with the two public datasets, CIFAR-10 and Flowers, and attain the accuracy of 95.46% and 95.57% for the test sets, respectively. The average recognition time for a single cotton leaf image is about 0.015 s. Furthermore, the mobile terminal application is developed with the model embedded, to realize the real-time recognition for cotton spider mites’ damage level in the field.