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.

本数据集收录了2024年于美国加利福尼亚州圣玛丽亚地区，使用搭载传感装置的采摘手推车开展人工草莓采摘作业期间采集的收获数据。该数据集包含经地理标记的采摘重量、手推车位置及运动状态信息，这些数据由GPS接收机、惯性测量单元（Inertial Measurement Unit，IMU）与称重传感器采集得到。每个数据点均标注为"Pick（表示正在进行主动采摘）"或"NoPick（表示未进行主动采摘）"。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过程中的作业人员活动状态，并量化其作业效率。对于致力于优化劳动力与田间管理、研发草莓采摘机械或采摘辅助系统的精准农业（precision agriculture）及农业自动化领域的研究者与从业者而言，本数据集具有重要的应用价值。