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.

翻译：本文提出了一种新颖的应用，即基于可穿戴传感器生成的时间序列数据检测采果工活动。采摘过程中，采果工将果实放入可穿戴采摘袋，并在果园内将其倒入采收箱。一旦采收箱装满，这些箱子会迅速被运送至冷却包装车间，以延长采摘果实的货架期。对农场主和管理者而言，掌握采摘袋何时被倒空的信息对于更有效地管理采收箱、最大限度地减少采摘果实暴露在高温下的时间（否则会导致货架期缩短）至关重要。本研究提出利用可穿戴传感器与机器学习方法进行人体活动识别，以检测这些倒袋事件。我们开发了一种半监督数据标注方法，并基于实际数据集测试了基于特征集的机器学习集成模型与深度递归卷积神经网络。对比结果显示，该神经网络的检测准确率达86%。