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Hyperion hyperspectral remote sensing is rapidly advancing as a leading technique in remote sensing due to its extensive applications and enhanced accuracy compared to traditional methods. This study examines the health of Arecanut crops in Channagiri, Davanagere, Karnataka, using Hyperion hyperspectral data collected between 2013 and 2016. The focus is on differentiating between stressed and healthy crops based on variations in reflectance in the red-edge region. By utilizing the narrow bands available in hyperspectral data, various vegetative indices were computed, including NDVI, SRI, EVI, ARVI, SGI, RENDVI, MRENDVI, VREI, REPI, NDWI, MSI, and NDII. Additionally, a specialized index known as the Disease Index (DI), developed for Arecanut crops in previous research, was used to assess disease severity. The study revealed that the DI’s effectiveness in identifying disease severity improved in 2016 compared to earlier years, suggesting that the remedial measures implemented in response to previous findings were beneficial to farmers. After applying the necessary atmospheric corrections and following standard protocols, the hyperspectral remote sensing data proved useful for tracking the health of the Arecanut crop. The results provide farmers with crucial information to identify stressed crops and make informed decisions for managing their fields.

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Assessing Arecanut Crop Health Using Hyperspectral Remote Sensing: Evaluating Disease Severity with Narrow-Band Indices and the Disease Index

  • 会议论文

  • 发表日期：

    2026年

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  • B. E. Bhojaraja,
  • Thanushree Hegde,
  • S. P. Snehitagouda,
  • Arunkumar Yadav

摘要

Hyperion hyperspectral remote sensing is rapidly advancing as a leading technique in remote sensing due to its extensive applications and enhanced accuracy compared to traditional methods. This study examines the health of Arecanut crops in Channagiri, Davanagere, Karnataka, using Hyperion hyperspectral data collected between 2013 and 2016. The focus is on differentiating between stressed and healthy crops based on variations in reflectance in the red-edge region. By utilizing the narrow bands available in hyperspectral data, various vegetative indices were computed, including NDVI, SRI, EVI, ARVI, SGI, RENDVI, MRENDVI, VREI, REPI, NDWI, MSI, and NDII. Additionally, a specialized index known as the Disease Index (DI), developed for Arecanut crops in previous research, was used to assess disease severity. The study revealed that the DI’s effectiveness in identifying disease severity improved in 2016 compared to earlier years, suggesting that the remedial measures implemented in response to previous findings were beneficial to farmers. After applying the necessary atmospheric corrections and following standard protocols, the hyperspectral remote sensing data proved useful for tracking the health of the Arecanut crop. The results provide farmers with crucial information to identify stressed crops and make informed decisions for managing their fields.