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This study proposes an artificial intelligence (AI) model that leverages B-scans for image classification, specifically targeting the accurate estimation of buried root organ orientation. The Convolutional Neural Network model exploits the intrinsic information within these images to analyze delicate features, enabling a rigorous classification of root orientations into 37 distinct categories for horizontal orientation ( \(\alpha \) ) and 19 categories for vertical inclination ( \(\beta \) ). This approach aims to significantly enhance the accuracy of root orientation estimation in subterranean environments, holding promising potential for practical applications, particularly in precision agriculture.

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Estimation of Root Orientation Using a Convolutional Neural Network Model

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammed Kahlaoui,
  • Aboulkacem Karkri,
  • Mohammed Anisse Moutaouekkil,
  • Chakib Taybi

摘要

This study proposes an artificial intelligence (AI) model that leverages B-scans for image classification, specifically targeting the accurate estimation of buried root organ orientation. The Convolutional Neural Network model exploits the intrinsic information within these images to analyze delicate features, enabling a rigorous classification of root orientations into 37 distinct categories for horizontal orientation ( \(\alpha \) ) and 19 categories for vertical inclination ( \(\beta \) ). This approach aims to significantly enhance the accuracy of root orientation estimation in subterranean environments, holding promising potential for practical applications, particularly in precision agriculture.