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Prediction of Moisture content in Maize leaves based on Hyperspectral Image

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  131618130,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: @2013
Authors:   Yu Sun, Di Wu, Wenting Han, Jifeng Ning
Keywords:   maize leaves moisture content hyperspectral partial least squares regression nondestructive detection

Abstract. The moisture content of maize leaves under different moisture treatment is predicted by the hyperspectral imaging technique. The reflectance spectral of maize leaves is acquired in the spectral range of 900 to 1700nm by hyperspectral image system. And the moisture content of the same samples is acquired by traditional baking method. Then the partial least square regression (PLSR), MLR and BP model were used to analyze the correlation between hyperspectral data and moisture content. Good correlations were found between moisture content of maize leaves and spectral information, the coefficient of determination is better than 0.85 with RMSEC and RMSEP less than 0.01. This work provides an effective method for prediction of moisture content of maize leaves by hyperspectral image technique.

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