Synygium cumini plant photosynthetic pigment detection from hyperspectral datasets using spectral indices

The identification photosynthetic pigment in synygium cumini plant species is needed for many applications such as plant species classification along with its current status and yield estimation. In the present study, we have investigated the study of photosynthetic pigment identification of synygium cumini plant species using spectral indices derived from hyperspectral data. The samples of synygium cumini plant were collected from the university campus area of Dr. B. A. M. U., Aurangabad positioned on 19°54'3.7944" N latitude and 75°21'8.9208" E longitude. The variations of leaf chlorophyll content, Chl a, Chl b and carotenoid along with water content, xanthophyll, anthocyanin were calculated of healthy leaves of plant based on spectral indices. The linear regression models were developed for the calculation of correlations between spectral indices and pigment contents using open source approach i.e. python. The results of the regression models illustrate the positive correlation between spectral indices and photosynthetic pigment contents in all species (mSR; R
²
= 0.157, BIG2; R
²
= 0.069, ARI; R
²
= 0.454). There were negative correlation between some spectral indices and species contents (mND; R
²
=0.051, PSNDa; 0.021, WI; R
²
= 0.028, X1; R
²
= 0.098) and neutral correction between (pSNDb; R
²
= 0.001, PSNDc; R
²
= 0.002, NDVI; R
²
= 0.033). In the conclusion, spectral indices and designed regression model is found accurate for estimating pigment and other contents using hyperspectral data.