梳理45个农业视觉数据集，助力精准农业研究
A survey of datasets for computer vision in agriculture
- 系统收集并整理45个田间图像数据集，覆盖多种作物与场景
- 发现公开高质量农业数据集稀缺，阻碍应用与通用模型发展
- 适合从事智慧农业、计算机视觉的科研人员参考使用
在农业研究中，计算机视觉（CV）相关工作近年来显著增加。然而，与通用计算机视觉研究不同，高质量的公开数据集仍然稀少。这部分归因于不同农业任务、作物和环境之间的高度差异性，以及数据采集的复杂性，同时也受制于许多作者不愿公开发布数据集。此外，缺乏广泛使用的农业数据仓库也制约了农业视觉研究的发展，以及农业数据在通用计算机视觉中的应用。本文综述了大量高质量的田间图像数据集，共发现45个数据集，均列于本文及项目网站（https://smartfarminglab.github.io/field_dataset_survey/）的在线目录中。
原文摘要 · Abstract (English)
In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.
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