AUTHOR=Wang Xiaodong , Du Jianming , Xie Chengjun , Wu Shilian , Ma Xiao , Liu Kang , Dong Shifeng , Chen Tianjiao 

TITLE=Prior knowledge auxiliary for few-shot pest detection in the wild

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.1033544

DOI=10.3389/fpls.2022.1033544

ISSN=1664-462X

ABSTRACT=One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost effective, and responsive in a timely manner. However, existing deep-learning-based methods can detect only a dozen or so common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest datasets for their base-class training and novel-class fine-tuning, and this hinders significantly the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios.  Firstly, a prior-knowledge-auxiliaried architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection dataset has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean Average Precision (mAP) and mean Average Recall (mAR). This experimental performance shows explicitly the promising effectiveness of the proposed few-shot detection architecture.