International Journal of Innovative Technology and Research
Abstract
numerous implications related with food, economic and environmental concerns are looking for cotton crop identification using timely information. However, the accuracy of cotton crop detection remains a challenging task, despite supervised classifier play significant roles. Moreover, the obtained results are not at expected remarkable level, when such direct application of classifiers is done on the images. Hence, vegetation indices are brought into the picture, but challenge persists on identifying suitable vegetation indices among numerous indices in the literature. This work studies the performance of such six renowned vegetation indices like Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalised IR, Wide-Dynamic Range Vegetation Index (WDRVI) and GreenNDVI (GNDVI). For this study, fuzzy c – means (FCM) clustering algorithm is unified with neural network, which is trained using Levenberg – Marquardt (LM) algorithm. The experimental results are investigated through which it is found that Green normalised difference Vegetation Index performs well than any other vegetation indices by accomplishing 91.63% accuracy on average
