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One of the key challenges in precision agriculture is accurately distinguishing crops from the surrounding soil. Contemporary algorithms in precision farming rely on multi-spectral or hyper-spectral data and artificial intelligence networks for computing radiometric indices, supporting the operational management of agricultural systems. These transformations act as natural filters for multi-spectral and hyper-spectral imagery, reducing the complexity of data input while improving the network’s ability to classify information. This study suggests defining the radiometric index with the help of a directional mathematical filter in order to accurately segment crops from soil.

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Multi-spectral Approach to Segment Remote Sensing Data

  • 会议论文

  • 发表日期：

    2026年

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  • Prasad Sundaramoorthy,
  • Bhanuprakash Madupati,
  • Rakesh Kumar Pal,
  • Tanvi Desai,
  • Tarak Hussain,
  • Abhilash Maroju

摘要

One of the key challenges in precision agriculture is accurately distinguishing crops from the surrounding soil. Contemporary algorithms in precision farming rely on multi-spectral or hyper-spectral data and artificial intelligence networks for computing radiometric indices, supporting the operational management of agricultural systems. These transformations act as natural filters for multi-spectral and hyper-spectral imagery, reducing the complexity of data input while improving the network’s ability to classify information. This study suggests defining the radiometric index with the help of a directional mathematical filter in order to accurately segment crops from soil.