An Image Dataset for Analyzing Tea Picking Behavior in Tea Plantations

Tea is a significant economic product in our country, and tea plantation harvesting constitutes an essential agricultural activity. The tea plantation picking work is gradually moving towards intelligence and mechanization. As an active research field, artificial intelligence recognition technology is expected to identify the large-scale tea plantation picking work that is being promoted under the current situation, as well as the identification of tea plantation picking behavior. This series of work is inseparable from the construction of datasets, but there is still a large gap in the current tea plantation picking data. The different behaviors and tools of picking personnel in the tea plantation picking scene have not been well distinguished and identified. Based on this blank area, this work establishes a dataset from online video slices of tea plantation scenes. The dataset consists of 12,195 sliced picking images featuring five different types of behaviors( list as: 1) pick; 2) pick(machinery); 3) walk; 4) talk; 5) stand) under five distinct environmental conditions: 1) sunny; 2) overcast; 3) cloudy; 4) foggy; 5) rainy. All labels for the dataset are provided in COCO format for public use.

茶叶乃我国重要的经济产品，茶园采摘作业构成一项至关重要的农业生产活动。茶园采摘作业正逐步迈向智能化与机械化。作为一项活跃的研究领域，人工智能识别技术有望识别在当前形势下推广的大规模茶园采摘作业，以及茶园采摘行为的识别。这一系列工作与数据集的建设密不可分，然而，当前茶园采摘数据仍存在较大缺口。茶园采摘场景中采摘人员的不同行为与工具尚未得到充分的区分与识别。基于这一空白领域，本研究从茶园场景的在线视频切片中构建了一个数据集。该数据集包含12,195张采摘切片图像，涵盖五种不同的行为类型（具体列示如下：1）采摘；2）机械采摘；3）行走；4）交谈；5）站立），并置于五种独特的环境条件下：1）晴朗；2）阴天；3）多云；4）雾天；5）雨天。数据集的所有标签均以COCO格式提供，以供公众使用。