Lightweight Automatic Identification and Location Detection Model of Farmland Pests

Automatic identification and location of farmland pests are an important direction of target detection research. The wide variety of pests and the similarity between pest categories make the automatic identification of farmland pests have some problems, such as high error rate and difficult identification. In order to achieve a better target for automatic identification and location of farmland pests, this paper proposes a lightweight pest detection model, and the network is the EfficientNet proposed by Google, which achieves the detection of 26 pests, the idea based on the classical Yolo target detection algorithm. First of all, features were extracted through the lightweight backbone, and then multiscale feature fusion is performed by PANet; finally, three feature matrices with different sizes were output to predict pests of different sizes. Using CIOU as the loss function of regression prediction better reflects the relative position of the prior box and the real box. The experimental results are compared with other lightweight algorithms, and the results show that the accuracy rate of the algorithm for identification and localization of agricultural pest in this paper is the highest and could reach 93.73%. Moreover, the model is lightweight and can be deployed on low-cost equipment, which reduces the cost of equipment and accurately predicts the status of pests and diseases in farmland. In practice, it is shown that the algorithm can effectively solve the problems of large number of pests, pest accumulation, background interference, and has strong robustness.