Crop Water Requirement Estimation for Rubber Plantation Using Hyperspectral Remote Sensing

Rubber is one of the primary plantations in India that cultivates a substantial amount of flora. The crop is extensively cultivated across the nation due to its significant market value. A section of the Madikeri taluk in the Kodagu region of Karnataka will be the area of interest for this research work. Precisely calculating the quantity of irrigation water required for the crop is crucial for boosting productivity, particularly in the summer, as well as for developing sustainable water resource management strategies. The entire amount of water required to stabilize evapotranspiration–a function of both crop and local climatic conditions–is known as crop water need or requirement (CWR). Computing crop evapotranspiration is a popular technique for calculating agricultural water requirements. Traditional methods of area-based forecasting are proved to be non-economical and tedious. Recent developments in hyperspectral technology for remote sensing can aid in achieving satisfactory results. In order to evaluate CWR for rubber plantation in the study area, crop coefficients are computed using an Normalized Difference Vegetation Index (NDVI)-based technique, and the reference crop evapotranspiration is computed using the Hargreaves and Samani equation. Based on these equations, the CWR for rubber plantation in the study area varied from a minimum of 0.45 to maximum of $2.97 \mathrm{~mm} /$ day.

Authors

Institutions

Publication Details

Published
2024-12-02
DOI
https://doi.org/10.1109/ingarss61818.2024.10984283
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Crop Water Requirement Estimation for Rubber Plantation Using Hyperspectral Remote Sensing

P. S. Prakash, B.E. Bhojaraja, M. C. Chandan
Smart Agriculture and AI
article

Crop Water Requirement Estimation for Rubber Plantation Using Hyperspectral Remote Sensing

P. S. Prakash, B.E. Bhojaraja, M. C. Chandan
article en

Abstract

Rubber is one of the primary plantations in India that cultivates a substantial amount of flora. The crop is extensively cultivated across the nation due to its significant market value. A section of the Madikeri taluk in the Kodagu region of Karnataka will be the area of interest for this research work. Precisely calculating the quantity of irrigation water required for the crop is crucial for boosting productivity, particularly in the summer, as well as for developing sustainable water resource management strategies. The entire amount of water required to stabilize evapotranspiration–a function of both crop and local climatic conditions–is known as crop water need or requirement (CWR). Computing crop evapotranspiration is a popular technique for calculating agricultural water requirements. Traditional methods of area-based forecasting are proved to be non-economical and tedious. Recent developments in hyperspectral technology for remote sensing can aid in achieving satisfactory results. In order to evaluate CWR for rubber plantation in the study area, crop coefficients are computed using an Normalized Difference Vegetation Index (NDVI)-based technique, and the reference crop evapotranspiration is computed using the Hargreaves and Samani equation. Based on these equations, the CWR for rubber plantation in the study area varied from a minimum of 0.45 to maximum of $2.97 \mathrm{~mm} /$ day.

Nitte University (IN), Irish Centre for High-End Computing (IE)
Zero hunger
Openalex Percentile: Top 84%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.