TY - GEN
T1 - Assessing Arecanut Crop Health Using Hyperspectral Remote Sensing
T2 - International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024
AU - Bhojaraja, B. E.
AU - Hegde, Thanushree
AU - Snehitagouda, S. P.
AU - Yadav, Arunkumar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105037574861
UR - https://www.scopus.com/pages/publications/105037574861#tab=citedBy
U2 - 10.1007/978-981-95-2030-5_29
DO - 10.1007/978-981-95-2030-5_29
M3 - Conference contribution
AN - SCOPUS:105037574861
SN - 9789819520299
T3 - Lecture Notes in Civil Engineering
SP - 355
EP - 365
BT - Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024
A2 - Van den bergh, Wim
A2 - Yaragal, Subhash C.
A2 - Prashanth, Shreelaxmi
A2 - Pandit, Poornachandra
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 December 2024 through 14 December 2024
ER -