Dataset for worker activity recognition and efficiency estimation during manual harvesting

This dataset contains harvest data collected during manual strawberry harvesting with instrumented picking carts in Santa Maria, CA, USA, in 2024. The data includes geo-tagged harvest mass, cart location, and motion recorded by a GPS receiver, an Inertial Measurement Unit (IMU), and load cells. Each data point is annotated as either "Pick" (indicating active picking) or "NoPick" (indicating no active picking). This dataset can be used to train, validate, and test AI algorithms to recognize worker activity during manual fruit harvesting and quantify worker efficiency. It is valuable for researchers and practitioners in precision agriculture and agricultural automation who are working on optimizing labor and field management, as well as developing strawberry harvesting machines or harvest assist systems.

本数据集包含2024年于美国加利福尼亚州圣玛丽亚市，使用配备传感采集装置的采摘手推车开展人工草莓采摘作业时采集的收获数据。该数据集涵盖经地理标记的采摘重量、手推车位置及运动数据，相关数据由GPS接收机（GPS receiver）、惯性测量单元（Inertial Measurement Unit，IMU）与称重传感器（load cells）采集得到。每个数据点均标注为"Pick"（表示正在进行主动采摘作业）或"NoPick"（表示未开展主动采摘作业）。本数据集可用于训练、验证与测试人工智能算法，以识别人工果品采摘场景下的作业人员活动状态，并量化作业人员的作业效率。本数据集面向精准农业（precision agriculture）与农业自动化领域的研究人员及从业者，具有较高应用价值，相关人员可依托该数据集优化劳动力与田间管理方案，同时研发草莓采摘机械或采摘辅助系统。