所属单位：计算机科学与工程学院

发表刊物：Frontiers in plant science

项目来源：自选课题

关键字：deep learning; forestry pest dataset; forestry pest identification; object detection; transformer.

摘要：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.

合写作者：刘陆洋,卓然,陈卫东,段睿,王贵参

第一作者：刘冰

论文类型：期刊论文

卷号：Vol.13

页面范围：1

ISSN号：1664-462X

是否译文：否

发表时间：2022-07-14