Recognition Algorithm of Agricultural Diseases and Insect Pests Based on PP-YOLO
ZHANG Yong
ZHAI Jincheng
WANG Lixiao
SONG Bingguo
CHEN Lei
Abstract：In order to solve the problem that the recognition degree of pests is relatively low due to the scale diversity of pests,this study proposed an agricultural pest identification algorithm based on PP-YOLO.A total of 2 359 sample data sets were selected,and the training set and test set were divided according to the ratio of 9∶1.The PP-YOLO model was selected for pest detection,and the model accuracy was evaluated by using map index.The small and medium-sized objectives of the method of PP-YOLO combined with the data enhancement mixup and color distortion were discussed applicability of detection.The map of the PP-YOLO model was 47.4%and 26.5%in the detection of small and medium-sized targets of diseases and insect pests.Based on the PP-YOLO model,the map was increased by 4.3%and 2.9%respectively after the combination of data enhancement mixup and color distortion.The PP-YOLO model proposed in this paper could effectively detect and identify crop pests.At the same time,data enhancement mixup and color distortion could effectively improve the data sample index of pests and diseases.
Keywords：Artificial intelligencepest identificationPP-YOLOdata enhancementcolor distortion
Publication Date：2024-05-20
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：8( 80-87 )