In this paper we present a novel application of detecting fruit picker
activities based on time series data generated from wearable sensors. During
harvesting, fruit pickers pick fruit into wearable bags and empty these bags
into harvesting bins located in the orchard. Once full, these bins are quickly
transported to a cooled pack house to improve the shelf life of picked fruits.
For farmers and managers, the knowledge of when a picker bag is emptied is
important for managing harvesting bins more effectively to minimise the time
the picked fruit is left out in the heat (resulting in reduced shelf life). We
propose a means to detect these bag-emptying events using human activity
recognition with wearable sensors and machine learning methods. We develop a
semi-supervised approach to labelling the data. A feature-based machine
learning ensemble model and a deep recurrent convolutional neural network are
developed and tested on a real-world dataset. When compared, the neural network
achieves 86% detection accuracy.

本文提出一种基于佩戴式传感器生成的时间序列数据的水果采摘者活动检测应用。通过使用人体活动识别以及机器学习方法检测袋子倒空事件，以便实现对采摘装置的管理并减少水果在室外暴露时间。测试结果显示，所提出的深度卷积神经网络可以在真实数据集上达到 86% 的检测准确率。