Liu Bing, Liu Luyang, Zhuo Ran, Chen Weidong, Duan Rui, Wang Guishen
School of Computer Science and Engineering, Changchun University of Technology, Changchun, China.
College of Computer Science and Technology, Jilin University, Changchun, China.
Front Plant Sci. 2022 Jul 14;13:857104. doi: 10.3389/fpls.2022.857104. eCollection 2022.
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.
森林害虫的识别对于森林害虫规模的防治具有重要意义。然而，现有的数据集主要集中在常见物体上，这限制了深度学习技术在特定领域（如农业）的应用。在本文中，我们收集了林业害虫的图像，并构建了一个用于林业害虫识别的数据集，称为林业害虫数据集。林业害虫数据集包含31类害虫及其不同形态。我们在这个数据集上进行了几个主流的目标检测实验。实验结果表明，该数据集在各种模型上都取得了良好的性能。我们希望我们的林业害虫数据集将来能帮助害虫防治和害虫检测领域的研究人员。
