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Analysis of Hyperspectral Scattering Images Using a Moment Method for Apple Firmness Prediction

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

Citation:  Transactions of the ASABE. 57(1): 75-83. (doi: 10.13031/trans.57.10155) @2014
Authors:   Qibing Zhu, Min Huang, Renfu Lu, Fernando Mendoza
Keywords:   Apple, Firmness, Hyperspectral scattering, Least squares support vector machine, Moment method, Partial least squares.

Abstract. This article reports on using a moment method to extract features from the hyperspectral scattering profiles for apple fruit firmness prediction. Hyperspectral scattering images between 500 and 1000 nm were acquired online, using a hyperspectral scattering system, for ‘Golden Delicious’, ’Jonagold’, and ‘Delicious’ apples harvested in 2009 and 2010. The zeroth-order moment (ZOM), which is equivalent to the mean reflectance, and the first-order moment (FOM) were calculated from the hyperspectral scattering profiles for each wavelength. Firmness prediction models were developed for the ZOM data, FOM data, and their combined data (Z-FOM) using partial least squares (PLS) and least squares support vector machine (LSSVM). The PLS models based on the Z-FOM data improved prediction results by 1.5% to 12.5% for the prediction set, compared with the PLS models using the ZOM data alone. The LSSVM models for the prediction set of Z-FOM data yielded better prediction results, with improvements of 8.6% to 21.2% over the PLS models for the ZOM data, 7.2% to 17.7% over the PLS models for the Z-FOM data, and 2.9% to 15.2% over the LSSVM models for the ZOM data. The Z-FOM method provided a simpler, faster, and effective means to extract features from the hyperspectral scattering profiles, and it has led to significant improvements in firmness prediction accuracy when used with either PLS or LSSVM.

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