Cotton yield prediction using drone derived LAI and chlorophyll content
DOI:
https://doi.org/10.54386/jam.v24i4.1770Keywords:
Drone, LAI, SPAD chlorophyll, Multispectral imageries, Vegetation indices, YieldAbstract
The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition costs, and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities for better crop management. This study predicted the seed cotton yield for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis and regression analysis were done for ground truth data and vegetation indices for validation and accuracy and also to find the best-performing indices. It was concluded that the Wide Dynamic Range Vegetation Index (WDRVI) showed a better correlation coefficient (R=0.959) with LAI ground truth data (R2=0.919). In contrast, the Modified Chlorophyll Absorption Ratio Index (MCARI) showed a better correlation coefficient (R=0.919) with SPAD chlorophyll ground truth data (R2=0.845). Then the best performing indices WDRVI and MCARI were further used for generating the yield model. High spatial resolution drone imageries for determining LAI and chlorophyll are reliable and rapid, as per the study. It helps to determine the LAI and chlorophyll at a spatial scale and their influence on yield production. This yield prediction was technical support for the widespread adoption and application of unmanned aerial vehicle (UAV) remote sensing in large-scale precision agriculture.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 P. SHANMUGAPRIYA, K. R. LATHA, S. PAZHANIVELAN, R. KUMARAPERUMAL, G. KARTHIKEYAN, N. S. SUDARMANIAN
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
