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Affiliation of Author(s):计算机科学与工程学院

Journal:Frontiers in plant science

Funded by:自选课题

Key Words:deep learning; forestry pest dataset; forestry pest identification; object detection; transformer.

Abstract:The identification of forest pests is of great significance to the prevention and control of the forest pests’ scale. However, existing datasets mainly focus on common objects, which limits the application of deep learning techniques in specific fields (such as agriculture). In this paper, we collected images of forestry pests and constructed a dataset for forestry pest identification, called Forestry Pest Dataset. The Forestry Pest Dataset contains 31 categories of pests and their different forms. We conduct several mainstream object detection experiments on this dataset. The experimental results show that the dataset achieves good performance on various models. We hope that our Forestry Pest Dataset will help researchers in the field of pest control and pest detection in the future.

Co-author:刘陆洋,卓然,陈卫东,段睿,wangguishen

First Author:Kevin Liu

Indexed by:Journal paper

Volume:Vol.13

Page Number:1

ISSN No.:1664-462X

Translation or Not:no

Date of Publication:2022-07-14