COTTON LEAF MOISTURE CONTENT PREDICTION USING NEAR INFRARED HYPERSPECTRAL IMAGES

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COTTON LEAF MOISTURE CONTENT PREDICTION USING NEAR INFRARED HYPERSPECTRAL IMAGES

Abstract

Near infrared (NIR) hyperspectral imaging has been used as a rapid non-destructive technique to predict moisture content of cotton. To improve the performance of predicting, we first find and validate the fact that the texture near the veins is continues and directional. And then we propose Three-Dimension Gabor Filter (TDGF) and its corresponding filterbank to describe the textures of cotton leaf. After that we construct two types of models based on partial least squares (PLS) regression. Experiments are conducted to predict the moisture content of Cotton, Sand different regression models based on different types of features are built for comparison. The results how that the proposed filterbank is able to detect the optimal direction of water flow and the model combining the spectrum and TDGF textures outperform the other comparative models.

Author

M.Mathumitha S. R. Barkunan

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International Journal of Advanced
Science and Engineering Research

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