A non-destructive oil palm ripeness recognition system using relative entropy
Computers and Electronics in Agriculture
This paper introduces a relative entropy based image processing approach for the non-destructive prediction of the maturity of oil palm fresh fruit bunches (FFB) which enables the determination of the correct time for harvesting. The results of an experimental study of applying the Kullback–Leibler distance to the problem of oil palm classification are presented. It is shown that the proposed algorithm has an excellent accuracy and it can be computed very fast. The overall proposed system is simple and useful for oil palm farmers and entrepreneurs.
Palm oil, Color discrimination, Fresh fruits, Kullback Leibler divergence, Kullback-Leibler distance, Non destructive, Non-destructive prediction, Recognition systems, Relative entropy, Color vision, Entropy, Fruits, Image processing, Histogram color discrimination, Information theory, Kullback-Leibler divergence, Nigrescens fruits, Virescens fruits
Attaphongse Taparugssanagorn, Siwaruk Siwamogsatham, and C. Pomalaza-Ráez (2015).
A non-destructive oil palm ripeness recognition system using relative entropy. Computers and Electronics in Agriculture.118, 340–349. Elsevier.