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In this paper we present a novel application of detecting fruit pickeractivities based on time series data generated from wearable sensors. Duringharvesting, fruit pickers pick fruit into wearable bags and empty these bagsinto harvesting bins located in the orchard. Once full, these bins are quicklytransported 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 isimportant for managing harvesting bins more effectively to minimise the timethe picked fruit is left out in the heat (resulting in reduced shelf life). Wepropose a means to detect these bag-emptying events using human activityrecognition with wearable sensors and machine learning methods. We develop asemi-supervised approach to labelling the data. A feature-based machinelearning ensemble model and a deep recurrent convolutional neural network aredeveloped and tested on a real-world dataset. When compared, the neural networkachieves 86% detection accuracy.

Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning