Abstract Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop production cycle to be performed automatically and efficiently. However, the scarcity of public image datasets remains a crucial bottleneck for fast prototyping and evaluation of computer vision and machine learning algorithms for the targeted tasks. Since 2015, a number of image datasets have been established and made publicly available to alleviate this bottleneck. Despite this progress, a dedicated survey on these datasets is still lacking. To fill this gap, this paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision agriculture, which include 15 datasets on weed control, 10 datasets on fruit detection, and 9 datasets on miscellaneous applications. We survey the main characteristics and applications of these datasets, and discuss the key considerations for creating high-quality public image datasets. This survey paper will be valuable for the research community on the selection of suitable image datasets for algorithm development and identification of where creation of new image datasets is needed to support precision agriculture.

中文翻译：

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精准农业计算机视觉任务公共数据集调查

摘要 近年来，计算机视觉技术引起了精准农业的极大兴趣。作为机器人和人工智能的核心，计算机视觉使作物生产周期中从种植到收获的各种任务能够自动高效地执行。然而，公共图像数据集的稀缺性仍然是针对目标任务的计算机视觉和机器学习算法的快速原型设计和评估的关键瓶颈。自 2015 年以来，已经建立并公开了许多图像数据集以缓解这一瓶颈。尽管取得了这些进展，但仍然缺乏对这些数据集的专门调查。为了填补这个空白，本文对在田间条件下收集的促进精准农业的公共图像数据集进行了首次全面但并非详尽的审查，其中包括 15 个杂草控制数据集、10 个水果检测数据集和 9 个杂项应用数据集。我们调查了这些数据集的主要特征和应用，并讨论了创建高质量公共图像数据集的关键考虑因素。这份调查论文对于研究界选择合适的图像数据集进行算法开发和确定需要在何处创建新的图像数据集以支持精准农业具有重要价值。我们调查了这些数据集的主要特征和应用，并讨论了创建高质量公共图像数据集的关键考虑因素。这份调查论文对于研究界选择合适的图像数据集进行算法开发和确定需要在何处创建新的图像数据集以支持精准农业具有重要价值。我们调查了这些数据集的主要特征和应用，并讨论了创建高质量公共图像数据集的关键考虑因素。这份调查论文对于研究界选择合适的图像数据集进行算法开发和确定需要在何处创建新的图像数据集以支持精准农业具有重要价值。