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：在本文中，我们提出了一种基于佩戴式传感器生成的时间序列数据来检测果子采摘者活动的新方法。在采摘过程中，果子采摘者将果子装入佩戴的袋子中，然后将这些袋子倒入果子采摘桶中。一旦桶满，这些桶就立即运送到冷却的包装厂，以提高采摘果子的保质期。对于农民和经理来说，了解何时倒空采摘袋对管理采摘桶更加有效，以最大程度地减少采摘果子在炎热环境中的放置时间（导致果子保质期降低）非常重要。我们提出了一种使用佩戴式传感器的人体活动识别和机器学习方法来检测这些倒空事件的方法。我们开发了一种半监督方法来标记数据。我们开发了一种基于特征的机器学习集成模型和一个深度递归卷积神经网络，并在真实数据集上进行了测试。在比较时，神经网络实现了86％的检测准确率。