Optical Characteristics of Oil Palm Fresh Fruits Bunch (FFB) Under Three Spectrum Regions Influence for Harvest Decision

In current practice, appearance was used to determine ripeness for oil palm fresh fruits bunch (FFB), that accompanied by detachment of fruit-lets from the bunch. The FFB from marihat clone harvested at five ripeness stages, under ripeness (F0), ripeness (F1, F2, F3), and over ripeness (F4). At every ripeness stages, differences of oil content and pigment accumulation were observed on the bunch. All samples recorded using a digital camera (10 Mpixel) from 2, 7, 10, and 15 meter distance, simulating variation of light intensity upon recording. During image recording, three lighting were used, namely ultraviolet lamp (320-380 nm), visible light lamp (400-700 nm) and infrared lamp (720-1100 nm), all have similar power output of 600watt. Camera point of view was set to cover a square area of 12,5cm by 12,5cm of the frontal area of FFB, each picture produced has 3888 by 2952 pixel. Image processing software created to extract digital RGB information from the images, and displayed the information in histogram. From the experiment, it was observed that the changes of intensity influence the RGB value of recorded image with reverse correlation, and longer wave light spectrum produce smaller RGB value.  The correlation model among image recording distance and RGB of the image display similar nature.  From three color channels, G represents better correlation for sample’s oil content determination.  Using UV and visible lighting, the FFB samples may be determined for harvest decision, up to seven meter observation distance.

[1]  Cheng Yee Low,et al.  Assessment of palm oil fresh fruit bunches using photogrammetric grading system , 2011 .

[2]  Oil palm in Indonesia. , 1987 .

[3]  M. Z. Abdullah,et al.  Stepwise Discriminant Analysis for Colour Grading of Oil Palm Using Machine Vision System , 2001 .

[4]  V. M. Salokhe,et al.  Automatic Non-destructive Quality Inspection System for Oil Palm Fruits , 2014 .

[5]  Tomohiro Takigawa,et al.  Hyperspectral imaging for nondestructive determination of internal qualities for oil palm (Elaeis guineensis Jacq. var. tenera) , 2009 .

[6]  Meftah Salem M. Alfatni,et al.  Oil palm fruit bunch grading system using red, green and blue digital number. , 2008 .

[7]  Peeyush Soni,et al.  Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB) Using Machine Vision and Spectral Analysis , 2013 .

[8]  Mohd. Zaid Abdullah,et al.  COLOR VISION SYSTEM FOR RIPENESS INSPECTION OF OIL PALM ELAEIS GUINEENSIS , 2002 .

[9]  Peeyush Soni,et al.  In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach , 2014 .

[10]  L. C. Guan,et al.  The applications of computer vision system and tomographic radar imaging for assessing physical properties of food , 2004 .

[11]  Y. Tan,et al.  Chemistry and biochemistry of palm oil. , 2000, Progress in lipid research.

[12]  Peeyush Soni,et al.  Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision , 2013 .

[13]  Reza Ehsani,et al.  Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system , 2012 .

[14]  Y. Tan,et al.  Crude palm oil characteristics and chlorophyll content. , 1997 .

[15]  Wan Ishak Wan Ismail,et al.  DEVELOPMENT OF IMAGING APPLICATION FOR OIL PALM FRUIT MATURITY PREDICTION , 2009 .

[16]  Siva Kumar Balasundram,et al.  Relationship between oil content and fruit surface color in oil palm (Elaeis guineensis Jacq.). , 2006 .

[17]  I. Adamson,et al.  Chlorophyll and carotenoid changes in ripening palm fruit, Elaeis guineënsis , 1984 .

[18]  R. M. Soom,et al.  Morphological changes of the cellular component of the developing palm fruit (Tenera: Elaeis guineensis). , 1990 .