Using Genetic Algorithm Neural Network on Near Infrared Spectral Data for Ripeness Grading of Oil Palm (Elaeis guineensis Jacq.) Fresh Fruit

Issue Date

12-2016

Abstract

Genetic Algorithm Neural Network (GANN) for multi-class was used to predict the ripeness grades of oil palm fresh fruit using Near Infrared (NIR) spectral data. NIR spectral data provide sufficient information about compound structure of samples from the near infrared light that passes through. The variables used in the GANN modeling process were the new variables obtained as a result of dimensional reduction from original NIR spectral data using Principal Component Analysis (PCA). Three statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the percentage (%) of good classification were used to assess adequacy of the GANN model. Based on the results, the GANN model created was precise enough to be used as the model calibration for this multi-class problem.

Source or Periodical Title

Information Processing in Agriculture

ISSN

2214-3173

Volume

3

Issue

4

Page

252-261

Document Type

Article

Physical Description

illustrations, tables, graphs

Language

English

Subject

Genetic algorithm, Near infrared spectroscopy, Neural network, Oil palm, Principal component analysis, Ripeness

Identifier

https://doi.org/10.1016/j.inpa.2016.10.001

Digital Copy

yes

Link to Full Text

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