Classification of Fresh N36 Pineapple Crop Using Image Processing Technique

Abstract:

Malaysia is one of the world pineapple producers besides Thailand, Philippine, Indonesia, Brazil and South Africa. The government encourage farmers to have more production to meet increasing demand for export. Most of the pineapple production activities is still in manual process and rely on labor workers. In this paper, we proposed a system that can be used in production house to automatically detect the maturity index of pineapple. We implement image processing method to determine the maturity of a pineapple based on yellowish skin color. Binary ellipse mask has been used for extracting region of interest (ROI) as well as morphology normalized RGB to filter out the background and unwanted pixel image. Finally, linear method using threshold values has been selected to classify the maturity index. 910 pineapple images has been used at the development and testing stage and we obtained promising result with 94.29% good classification rate.

• Periodical: Advanced Materials Research (Volumes 418-420)
• Pages: 1739-1743
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.418-420.1739
• Online since: December 2011
• Authors: Shuhairie Mohammad, Kamarul Hawari Ghazali, Nazriyah Che Zan, Siti Sofiah Mohd Radzi, Rohana Abdul Karim
• Keywords: Machine Vision, Normalized RGB, Pineapple Maturity Classification

References

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