Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

The dataset The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations. Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood). The dataset comprises two subsets:- a subset we suggest using for the training/validation phases (contained in the `train/` folder)- a subset we suggest using for the test phase (contained in the `test/` folder) Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:- *imageName* - filename of the image containing the whiteflies,- *X,Y* - 2D coordinates of the whitefly in the image space,- *class* - class index of the insect (always 0 in this dataset). Citing our work If you found this dataset useful, please cite the following paper @inproceedings{CIAMPI2023102384, title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps}, journal = {Ecological Informatics}, volume = {78}, pages = {102384}, year = {2023}, issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } and this Zenodo Dataset @dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {https://doi.org/10.5281/zenodo.6560823} } Contact Information If you would like further information about the dataset or if you experience any issues downloading files, please contact us at [e-mail]# 数据集概述 害虫黄色色诱粘板（Pest Sticky Traps, PST）数据集是一类黄色色诱粘板图像的集合，专门用于训练与测试深度学习模型，以实现昆虫自动计数及害虫种群密度估算。 本数据集的图像由意大利比萨大学农业、食品与环境系的专家进行人工标注：在每只已识别昆虫的质心位置标注圆点。具体而言，本次标注将目标昆虫归为"粉虱（whitefly）"类别，涵盖两个不同物种：烟粉虱（*Bemisia tabaci*，Gennadius）与温室白粉虱（*Trialeurodes vaporariorum*，Westwood）。 本数据集包含两个子集： 1. 建议用于训练与验证阶段的子集，存放于`train/`文件夹中； 2. 建议用于测试阶段的子集，存放于`test/`文件夹中。 两个子集的标注信息分别存储于`train/annotations.csv`与`test/annotations.csv`文件中，标注文件包含以下字段： - *imageName*：包含粉虱的图像文件名 - *X,Y*：图像坐标系下粉虱的二维坐标 - *class*：昆虫的类别索引（本数据集中该值恒为0） ## 引用说明 若您认为本数据集对研究有所帮助，请引用以下论文： bibtex @inproceedings{CIAMPI2023102384, title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps}, journal = {Ecological Informatics}, volume = {78}, pages = {102384}, year = {2023}, issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } 同时请引用本Zenodo数据集： bibtex @dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {https://doi.org/10.5281/zenodo.6560823} } ## 联系方式 若您需要获取本数据集的更多信息，或在下载文件时遇到任何问题，请联系：`[e-mail]`