Paper Info
Title
Adaptive feature fusion pyramid network for multi-classes agricultural pest detection
Abstract
The accurate and robust crop pest detection system is an important step to enable the reliable forecasting of agricultural pest in the community of precision agriculture, attracting great attention in many countries. For achieving the automatic recognition and detection of agricultural pest, previous methods adopt image processing-based methods, leading to lower efficiency. Then, machine vision-based methods are introduced into crop pest detection by using hand-crafted feature descriptors, improving the detection precision and speed. However, the manual feature is powerless for precise recognition. Considering powerful ability of feature extraction of convolutional neural network(CNN), we have developed a CNN-based method for multi-classes pest detection under complex scenes. In this paper, an adaptive feature fusion is introduced into feature pyramid network for extracting richer pest features. Then, an adaptive augmentation module has been developed for reducing the information loss of the highest-level feature maps. Finally, a two-stage region-based convolutional neural network (R-CNN) was built for refining predicted bounding boxes, which can obtain the categories and locations of pests of each image. We have conducted large quantities of comparison experiments on AgriPest21 dataset. Our method could achieve an accuracy of 77.0%, which significantly outperforms other state-of-the-art methods, including SSD, RetinaNet, FPN, Dynamic R-CNN, and Cascade R-CNN.
Year
DOI
Venue
2022
10.1016/j.compag.2022.106827
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Keywords
DocType
Volume
Agricultural pests, Object detection, Precision agriculture, Convolutional neural network, Adaptive fusion
Journal
195
ISSN
Citations 
PageRank 
0168-1699
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Lin Jiao132.44
Chengjun Xie200.34
Chen Peng31881121.56
Jianming Du400.34
Li Rui5215.56
Jie Zhang620514.03