Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

N. Hashim, . and Adebayo S. E., . Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network. pp. 144-151. ISSN 2550-2166

In this study the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532 660 785 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption m‚� and reduced scattering m coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC) root mean square error of crossvalidation (RMSECV) correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the m‚� and m with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

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Abstract

In this study the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532 660 785 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption m‚� and reduced scattering m coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC) root mean square error of crossvalidation (RMSECV) correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the m‚� and m with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

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Item Type:	Article
AGROVOC Term:	Pears
AGROVOC Term:	Fresh fruits
AGROVOC Term:	Maturity
AGROVOC Term:	Sampling
AGROVOC Term:	Job performance
AGROVOC Term:	Quality controls
AGROVOC Term:	Optical properties
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/10575

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