Quantum Neural Network-Based Deep Learning System to Classify Physical Timeline Acceleration Data of Agricultural Workers

Abstract

In this study, we extract agricultural (agri-) workers’ physical acceleration timeline data and execute quantum neural network-based deep learning for data classification. Although various approaches have been implemented globally for indoor and outdoor agri-working sites, there is still scope for improvement. Therefore, in this study, we adapt these approaches mainly for automated, high-tech agri-sites and apply the quantum convolutional neural network (QCNN) deep learning-based method to qualitatively demonstrate the classification of physical workers’ timeline datasets. For our dataset, our subjects were six experienced and six completely inexperienced male agri-manual workers. The target task was to cultivate a farmland using a simple traditional Japanese hoe in a semi-crouching position. We captured the subjects’ acceleration data using an integrated multi-sensor module mounted on a wooden lilt 150 mm from the gripping position of the dominant hand. We developed and used Python (ver. 3.9) and recent distributed libraries for quantum computation and distributed concerned libraries. For data classification, we successively executed three patterns using QCNN, a category of quantum deep learning (QDL)-based deep learning, and evaluated the systems by obtaining loss, validation accuracy, and final epochs’ elapsed time data. These methods of analyzing digital data are expected to find practical applications and provide key suggestions for improving daily task efficiency.