基于ELI5、PDPbox和Skater的可解释人工智能(XAI)对农业数据模型的适应性研究

Shinji Kawakura, M. Hirafuji, S. Ninomiya, R. Shibasaki
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引用次数: 3

摘要

我们使用基于Explain Like I 'm 5 (ELI5)、Partial Dependency Plot box (PDPbox)和Skater的可解释人工智能(XAI)来分析各种物理农业(agri-)工人数据集。我们开发了各种有前途的身体传感系统,以促进农业技术进步、培训和工人发展以及安全。这包括可穿戴传感系统(wss),它可以通过分析不同农业环境(如田地、草地和花园)中的人体动力学和统计数据,捕获与农业工人运动相关的实时三轴加速度和角速度数据。在使用Python编写的新程序调查获得的时间序列数据后,我们与真正的农业工人和管理人员讨论了我们的发现和建议。在本研究中,我们使用XAI和可视化分析不同的数据,有经验和没有经验的农业工人,以开发一种适用于农业主管培训农业工人的方法。
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Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data
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.
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