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Kumaraperumal, R.
- Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices
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PDF Views:89
Authors
P. Shanmugapriya
1,
K. R. Latha
1,
S. Pazhanivelan
2,
R. Kumaraperumal
3,
G. Karthikeyan
4,
N. S. Sudarmanian
5
Affiliations
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
2 Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
3 Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
4 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
5 Krishi Vigyan Kendra, Aruppukottai 626 107, India, IN
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
2 Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
3 Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
4 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
5 Krishi Vigyan Kendra, Aruppukottai 626 107, India, IN
Source
Current Science, Vol 123, No 12 (2022), Pagination: 1473-1480Abstract
Crop health monitoring and assessment have become more successful with the advent of remote sensing technology in agriculture. Using this technology, retrieving information about crop biophysical parameters on a non-destructive basis at spatial and temporal scales has been made possible. Several drone-derived spectral vegetation indices (VIs) have assessed crop growth status in a larger farming area. In this study, we generated VI maps for a cotton field area in the Tamil Nadu Agricultural University, Coimbatore, India. The ground-truth chlorophyll data (SPAD-502 Minolta meter) were collected from the field on the same day of drone image acquisition. Pearson correlation analysis and regression analysis were done for validation and accuracy of the ground-truth chlorophyll data and VIs. The study reveals that obtaining near real-time chlorophyll content using high spatial resolution drone images is quick and reliableKeywords
Chlorophyll content, cotton crop, drone, multi-spectral images, spectral indices.Full Text
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