A Dataset for Forestry Pest Identification

作者：Bing Liu, Luyang Liu, Ran Zhuo, Weidong Chen, Rui Duan, Guishen Wang · 发表于：Frontiers in Plant Science · 年份：2022 · DOI：10.3389/fpls.2022.857104 · 被引用次数：22 · 研究领域：Date Palm Research Studies、Insect and Arachnid Ecology and Behavior、Forest Insect Ecology and Management

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.