Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices

Crop health monitoring and assessment have become more successful with the advent of remote sensing technology in agriculture. The near-ground remote sensing (drone) technique has provided broader agronomic applications for better crop management. 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 (VI) have assessed crop growth status in a larger farming area. This study generated vegetation indices maps for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. The ground truth chlorophyll data (SPAD-502 Minolta meter) was collected on the same day of drone image acquisition. Vegetation indices viz., Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge Index (NDRE), Red Edge Difference Vegetation Index (REDVI), Optimized Soil Adjusted Vegetation Index (OSAVI) and Modified Chlorophyll Absorption Ratio Index (MCARI) were derived from the five multispectral bands viz., red, green, blue, near-infrared and red edge. The data were processed using Pix4Dmapper software, and vegetation indices were derived using ArcGIS 10.6 software. Pearson correlation analysis and regression analysis were done for validation and accuracy for ground truth chlorophyll data and vegetation indices. It was concluded that the Modified Chlorophyll absorption Ratio Index showed a better correlation coefficient (R=0.933) with ground truth chlorophyll data with an R 2 value of 0.87. The study revealed that obtaining near real time chlorophyll content using high spatial resolution drone images is reliable and quick. This information is very helpful in generating the chlorophyll map, which helps regulate the nutrient requirements at spatial variability. near-infrared for visualization of Indices are defined as a ratio of the difference between the reflectance of different spectral bands, which provide different data layers these help in monitoring the crop growth as they can enhance the spectral differences at specific 7 . The index is a data processing method vegetation spectrum information by using different linear combinations of the ratio between the visible and near-infrared fundamental for is that plant has a high sensitivity to the visible and near-infrared wavelengths, and the combination of these two bands is very effective. They strengthen the information about closed vegetation. very and values in some of the that it is sensitive to non-photosynthetic materials and properties the red edge values are higher values some