COLOR VISION SYSTEM FOR RIPENESS INSPECTION OF OIL PALM ELAEIS GUINEENSIS

By M. Abdullah, L. Guan, A. Mohamed, M. A. M. Noor. 2002

50 citations recorded by the source corpus.

In checking harvesting discipline and quality control for oil palm fruits, color has presumably been an important guide to whether the oil content has reached a maximum where the fruit bunch is ready for cutting. However, establishing a single and harmonious standard base on color is a very contentious issue in the oil palm industry because of the subjective nature of the human vision of color. This was further complicated due to the lack of information on fruit color upon which to base a definite ripeness criterion. We demonstrated in this paper that this problem can be solved using machine vision technology. Methods used were to treat color in HSI (Hue, Saturation and Intensity) color space and applied multivariate discriminant analysis. These have proven to be highly effective for color evaluation and image processing. The vision system was trained to classify oil palms into four quality grades according to PORIM (Palm Oil Research Institute of Malaysia) inspection standards. These are the unripe, the underripe, the optimally ripe and the overripe classes. Depending upon the quality feature evaluated, misclassification by the vision system varied from 5 to 12% but averaged at about 8%. Machine vision disagreement ranged from 2 to 19%. L'industrie du palmier a huile utilise l'inspection visuelle de fruits pour definir le stade de maturite approprie pour la production d'huile. Cette etude a pour but de developper un systeme d'inspection assiste par ordinateur pour classifier les fruits du palmier a huile en quatre categories selon leur couleur: non mur, pas assez mur, bien mur et trop mur. Les erreurs de classification du systeme evalue varient de 5 a 12%.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/bb06bb5b29481d0ef7195521f6535c664d3c1d1b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Chunjiang Zhao
2. Wenqian Huang
3. Shuxiang Fan
4. Jiangbo Li

In Plant Science

1. Wenqian Huang
2. Shuxiang Fan

Citations in the corpus

• Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review (2014, background): “Star fruit Shape discrimination Fourier descriptors 100% Abdullah et al. (2006)”