Analyses of Diverse Agricultural Worker Data with Explainable Artificial Intelligence: XAI based on SHAP, LIME, and LightGBM

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  •   Shinji Kawakura

  •   Masayuki Hirafuji

  •   Seishi Ninomiya

  •   Ryosuke Shibasaki

Abstract

We use recent explainable artificial intelligence (XAI) based on SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light Gradient Boosting Machine (LightGBM) to analyze diverse physical agricultural (agri-) worker datasets. We have developed various promising body-sensing systems to enhance agri-technical advancement, training and worker development, and security. However, existing methods and systems are not sufficient for in-depth analysis of human motion. Thus, we have also developed wearable sensing systems (WS) that can capture real-time three-axis acceleration and angular velocity data related to agri-worker motion by analyzing human dynamics and statistics in different agri-fields, meadows, and gardens. After investigating the obtained time-series data using a novel program written in Python, we discuss our findings and recommendations with real agri-workers and managers. In this study, we use XAI and visualization to analyze diverse data of experienced and inexperienced agri-workers to develop an applied method for agri-directors to train agri-workers.


Keywords: Agricultural Worker Data, Explainable Artificial Intelligence, SHapley Additive Explanations, Local Interpretable Model-agnostic Explanations, Light Gradient Boosting Machine

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How to Cite
Kawakura, S., Hirafuji, M., Ninomiya, S., & Shibasaki, R. (2022). Analyses of Diverse Agricultural Worker Data with Explainable Artificial Intelligence: XAI based on SHAP, LIME, and LightGBM. European Journal of Agriculture and Food Sciences, 4(6), 11–19. https://doi.org/10.24018/ejfood.2022.4.6.348