Explainability Is Necessary for AI’s Trustworthiness

Philosophy & Technology, Journal Year: 2025, Volume and Issue: 38(1)

Published: Feb. 5, 2025

Language: Английский

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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 75 - 75

Published: March 10, 2025

Enhancing agricultural machinery field efficiency offers substantial benefits for farm management by optimizing the available resources, thereby reducing cost, maximizing productivity, and supporting sustainability. Field is influenced several unpredictable stochastic factors that are difficult to determine due inherent variability in configurations operational conditions. This study aimed simplify estimation training machine learning regression algorithms on data generated from a information system covering combination of different areas shapes, working patterns, machine-related parameters. The gradient-boosting regression-based model was most effective, achieving high mean R2 value 0.931 predicting efficiency, taking into account only basic geometric indices. developed showed also strong predictive performance indicative fields located Europe North America, considerably computational time an average 73.4% compared corresponding analytical approach. Overall, results this highlight potential simplifying prediction without requiring detailed knowledge plethora variables associated with operations. can be particularly valuable farmers who need make informed decisions about resource allocation planning.

Language: Английский

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Reflections and attentiveness on eXplainable Artificial Intelligence (XAI). The journey ahead from criticisms to human-AI collaboration

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103133 - 103133

Published: March 1, 2025

Language: Английский

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