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Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data

Author

Listed:
  • Shinji Kawakura

    (Osaka Metropolitan University/Osaka City, Japan)

  • Masayuki Hirafuji

    (University of Tokyo/Bunkyo-ku, Japan)

  • Seishi Ninomiya

    (University of Tokyo/Bunkyo-ku, Japan)

  • Ryosuke Shibasaki

    (University of Tokyo/Bunkyo-ku and University of Tokyo/Kashiwa-shi, Japan)

Abstract

We use explainable artificial intelligence (XAI) based on Explain Like I’m 5 (ELI5), Partial Dependency Plot box (PDPbox), and Skater 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. This includes wearable sensing systems (WSSs) 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-environments, such as 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.

Suggested Citation

Handle: RePEc:epw:ejai00:v:1:y:2022:i:3:id:1014
DOI: 10.24018/ejai.2022.1.3.14
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