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Reseach Article
Mango Fruit Quality Prediction using Associative Classification Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 16 |
| Year of Publication: 2012 |
| Authors: Rattapol Pornprasit, Juggapong Natwichai, Bowonsak Srisungsittisunti |
10.5120/9198-3711
|
Rattapol Pornprasit, Juggapong Natwichai, Bowonsak Srisungsittisunti . Mango Fruit Quality Prediction using Associative Classification Rules. International Journal of Computer Applications. 57, 16 ( November 2012), 20-25. DOI=10.5120/9198-3711
Abstract
Near-infrared (NIR) spectroscopy is a non-destructive technique which can provide the quality measurement for agriculture products. In this paper, we propose an approach to utilize the NIR spectrum for mango fruits quality prediction. The prediction model is based on one of the most prominent machine learning approaches, associative classification. The associative classifiers are trained from the spectrum data of each mango fruit, and the chemical property represented fruit quality as the class label. When a classifier is to be applied to predict the quality, the spectrum of the mango fruits is measured, and the class label is determined by the classification rules subsequently. Series of experiments were conducted under various parameter settings to evaluate the accuracy of the prediction. The results showed that the highest accuracy, the optimal performance, can be obtained when the number of boxes, the number of partitions of each spectrum for rule generation, was set at 10, and the minimum support threshold and the minimum confidence threshold were set at 1% and 50%, respectively. Based on the thorough experiments, a guideline for optimal parameter determination is also proposed for the practitioners.
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