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EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR PINEAPPLE GRADING
Author(s) -
BOONMUNG SUWANEE,
CHOMTEE BOONORM,
KANLAYASIRI KANNACHAI
Publication year - 2006
Publication title -
journal of texture studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.593
H-Index - 54
eISSN - 1745-4603
pISSN - 0022-4901
DOI - 10.1111/j.1745-4603.2006.00069.x
Subject(s) - artificial neural network , grading (engineering) , artificial intelligence , computer science , engineering , civil engineering
The objective of this study is to evaluate resonant frequency, firmness and soluble solids for pineapple classification using artificial neural networks (ANNs) as the analytical tool. A sample of 149 pineapples was classified based on their internal qualities into five classes: unripe, partially ripe, ripe, partially overripe and completely overripe. The developed ANN model successfully classified pineapples into merely three classes as unripe, ripe and completely overripe. The most effective model was obtained when both resonant frequency and soluble solids were included in the model. The classification accuracy was more than 83% for all three classes.