| Popis: |
In this paper, the purpose of this paper is to realize the automatic detection of many kinds of diseases in balsam pear leaves, and a target detection algorithm based on Faster R-CNN is proposed to detect the diseases of balsam pear leaves in natural environment. In this algorithm, the pre-trained ImageNet depth network model is used for migration learning, and the three convolution neural networks ZF-Net, VGG_CNN_M_1024, VGG-16 are used as the feature extraction networks of this experiment. Combined with the small size of bitter gourd leaf disease, the parameters of the original faster R-CNN are modified to increase the recommended size of the area during training. The results showed that the deep learning network model trained with VGG-16 as feature extraction network had the best performance. The average accuracy rates of healthy leaves, powdery mildew, gray spot, vine blight and gray spot were 0.899, 0.830, 0.819, 0.795, and the average mean precision (mean average precision, mAP, was 0.836) after increasing the size of candidate frame, the MAP value of the model was 0.999%, which was increased by 7%. This method can effectively realize the classification and location of balsam pear leaf diseases and has important research significance for the prevention of melon and fruit diseases. |
