9 research outputs found
Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices
The quality and yield of tea depends upon management of tea plantations, which takes into account the
factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence.
Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation,
the present study was conducted to explore applicability of such data in evaluating these factors. Also
stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate
bands for accessing the above mentioned factors. The Green region followed by NIR region was
found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit
and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy
bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned
and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the
plantation that need special care and may be an indicator of tea productivity. The spectral signature of
these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to
derive these parameters at regional scale