SPECTRAL MONITORING AND YIELD PREDICTION OF COTTON

Abstract

Remote sensing technology is an important tool for monitoring crop growth and development. It also provides accurate and timely information about the crop yield. Result showed that LAI, plant height, total dry matter and dry matter partitioning are significantly correlated with spectral indices like radiance ratio (RR) and normalized difference vegetation index (NDVI). The correlation coefficient (r) values are improved with second degree model over that of simple linear model and are further improved under multiple linear model, in which days after sowing (DAS) was added as independent variable. Spectral indices and plant growth parameters except LAI and chlorophyll content are better correlated with Desi cotton as compared to American cotton and vice versa. The integrated spectral indices and plant growth variables are highly significantly correlated during 51-80 DAS Yield attributes like number of flowers per plant, number of unopened bolls/plant, number of total bolls/plant have been found more significantly correlated with spectral indices during 81-110 DAS The quadritic regression equation for American and Desi cotton yield over integrated spectral indices in different time segment periods show that American cotton is best correlated with RR and Desi cotton is best correlated with NDVI. The minimum percent diviaton between predicted yield from spectral parameters and actual yield are obtained during 81-110 DAS. It showed that the yield of American or Desi cotton could be estimated from spectral parameters during maximum vegetable growth stage with maximum 5 per cent deviation from actual yield.