关键词: 卷积神经网络, 水稻虫害, Faster-RCNN, SSD, YOLOv3

Abstract: In order to solve the problem of rice insect pests of artificial identification difficult and time-consuming, an automatic identification method of rice insect pests was studied. Rice pests images of Diostrombus politus Uhler, Nezara viridula, Naranga aenescens Moore, Riptortus pedestris, Sesamia inferens were selected from 2 500 images to construct weed rice pests set, and the features of the rice pest dataset were automatically extracted using deep convolutional neural network, and then the optimal weights of documents were got by training the stochastic gradient descent method. During the test, the trained model was used to select test sets, and YOLOv3 was also compared with Faster-RCNN and SSD algorithm. The results showed that average precision (AP) of YOLOv3 algorithm was the highest among all the deep learning models. the AP of Diostrombu politus Uhler, Nezara viridula,Naranga aenescens Moore, Riptortus pedestris, Sesamia inferens were 97.40%, 88.76%, 85.74%, 92.96% and 94.78%, respectively, and mAP in the five kinds of rice insect pests was 91.93%. The mAP of YOLOv3 increased by 1.43 percent and the detection time per image decreased by 833.68 ms compared with Faster-RCNN algorithm. The mAP of proposed method was 5.56 percent higher than that of SSD algorithm, and the detection time per image decreased by 2.90 ms. Five types of relatively representative images of rice insect pests were selected for testing. For the cases of leaf occlusion targets and similar backgrounds, YOLOv3 algorithm could correctly identify without missing or wrong detection, and the recognition precision was greater than 98%. It was feasible to introduce YOLOv3 algorithm into the identification of rice insect pests under complex field conditions, and it had higher precision and faster detection speed, which can accurately identify rice insect pests. It was of great significance for promoting fine cultivation of rice pest control and variable field spraying.

Key words: convolution neural network, rice pest, faster-RCNN, SSD, YOLOv3