Ong Song-Quan, Høye Toke Thomas
Department of Ecoscience Aarhus University, C. F. Møllers Allé 8, DK-8000 Aarhus C, Denmark.
Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah Malaysia.
Data Brief. 2024 Jul 14;55:110741. doi: 10.1016/j.dib.2024.110741. eCollection 2024 Aug.
The sticky trap is probably the most cost-effective tool for catching insect pests, but the identification and counting of insects on sticky traps is very labour-intensive. When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model - the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. As far as we know, there are no available image datasets to study these two aspects in computer vision and deep learning algorithms. Therefore, this paper presents a new dataset consisting of images of two pests commonly found in post-harvest crops - the red flour beetle () and the rice weevil () - captured with three different devices (DSLR, webcam and smartphone) on blue, yellow, white and transparent sticky traps. The images were sorted by device, colour and species and divided into training, validation and test parts for the development of the deep learning model.
粘虫板可能是捕获害虫最具成本效益的工具，但对粘虫板上昆虫的识别和计数非常耗费人力。在利用计算机视觉和机器学习研究粘虫板上害虫的自动识别和计数时，有两个方面会强烈影响模型的性能——粘虫板的颜色以及用于捕捉粘虫板上害虫图像的设备。据我们所知，在计算机视觉和深度学习算法中，没有可用的图像数据集来研究这两个方面。因此，本文提出了一个新的数据集，该数据集由收获后作物中常见的两种害虫——赤拟谷盗（）和米象（）的图像组成，这些图像是用三种不同设备（数码单反相机、网络摄像头和智能手机）在蓝色、黄色、白色和透明粘虫板上拍摄的。图像按设备、颜色和物种进行分类，并分为训练、验证和测试部分，用于深度学习模型的开发。
