Determination of RGB and Grayscale Value On Palm Oil (Elaeis guineensis Jacq.) Fresh Fruit Bunch (FFB) Images Using MATLAB
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Published:
Apr 28, 2022
Keywords:
Fresh Fruit Bunches, smartphone camera, R, G, B and GS color correlation
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Abstract
The Color and appearance are the most important indicators for a farmer to determine the condition of oil palm fresh fruit bunches (FFB) in the harvesting process. As the main attributes, the color and appearance become the guidelines for the initial assessment of fresh fruit bunches (FFB) of oil palm condition that is suitable or not for harvesting. In Facts, the FFB assessment activities are still carried out manually by utilizing the farmers visual, which is it will be prone to errors during the assessment. Due to this problem, it is important to automate the assessment of the color characteristics of oil palm FFB in order to minimize errors by farmers. The purpose of this study was to determine the efficiency of RGB color imaging techniques on oil palm FFB. The method used is processing and color analysis on 30 FFB images based on smartphone cameras to find the correlations for each color. Each color channel R, G and B in the FFB images was extracted and converted into grayscale using MATLAB R2021 software. The results show that the correlation value of R channel and grayscale has the highest value with R2 = 0.9569. This correlation is expected to be an initial study and suitable alternative for automating the assessment of the condition of oil palm FFB.
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Nasution, M. A., Winata, H. N., Nasution, F. Q., Lydiasari, H., Pasaribu, R. Y., Nasution, A., & Wulandari, A. (2022). Determination of RGB and Grayscale Value On Palm Oil (Elaeis guineensis Jacq.) Fresh Fruit Bunch (FFB) Images Using MATLAB. Jurnal Penelitian Kelapa Sawit, 30(1), 37-48. https://doi.org/10.22302/iopri.jur.jpks.v30i1.159
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- Indonesian Journal of Oil Palm Research can be accessed freely by anyone (open access) to introduce more journals to the public.
- The results of the research can be used freely with the inclusion of Indonesian Journal of Oil Palm Research as a source of utilization.
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Tan, Y. A., Low, K. W., Lee, C. K., & Low, K. S. (2010). Imaging technique for quantification of oil palm fruit ripeness and oil content. European Journal of Lipid Science and Technology, 112(8), 838–843.
Taparugssanagorn, A., Siwamogsatham, S., & Pomalaza-Ráez, C. (2015). A non-destructive oil palm ripeness recognition system using relative entropy. Computers and Electronics in Agriculture, 118, 340–349.
Winata, H. N., Nasution, M. A., Ahamed, T., & Noguchi, R. (2021). Prediction of concentration for microalgae using image analysis. Multimedia Tools and Applications, 80(6), 8541–8561. https://doi.org/10.1007/s11042-020-10052-y
Winata, H. N., Noguchi, R., Tofael, A., & Nasution, M. A. (2019). Prediction of Microalgae Total Solid Concentration by Using Image Pattern Technique. Journal of the Japan Institute of Energy, 98(5), 73–84. https://doi.org/10.3775/jie.98.73
Ali, M. M., Hashim, N., & Hamid, A. S. A. (2020). Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity. Computers and Electronics in Agriculture, 169, 105235.
Ansari, N., Lodha, A., Pandya, A., Sutariya, P. G., & Menon, S. K. (2015). Lab-on-phone citrate-capped silver nanosensor for lidocaine hydrochloride detection from a biological matrix. Analytical Methods, 7(21), 9084–9091.
Fahmi, F., Trianda, D., Andayani, U., & Siregar, B. (2018). Image processing analysis of geospatial uav orthophotos for palm oil plantation monitoring. Journal of Physics: Conference Series, 978(1), 12064.
Ishak, W. I. W., & Hudzari, R. M. (2010). Image based modeling for oil palm fruit maturity prediction. Journal of Food, Agriculture & Environment, 8(2), 469–476.
Jana, S., Parekh, R., & Sarkar, B. (2020). A De novo approach for automatic volume and mass estimation of fruits and vegetables. Optik, 200, 163443.
Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., & Seman, I. A. (2018). Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy. Computers and Electronics in Agriculture, 144, 297–309.
Makky, M., & Soni, P. (2014). In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach. Journal of Food Engineering, 120, 248–259.
Makky, M., & Soni, P. (2013). Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision. Computers and Electronics in Agriculture, 93, 129–139.
Muhammad Makky. (2016). A Portable Low-cost Non-destructive Ripeness Inspection for Oil Palm FFB. Agriculture and Agricultural Science Procedia, 9, 230–240.
Muhamad Saufi Mohd Kassim, Wan Ishak Wan Ismail, Abdul Rahman Ramli, Siti Khairunniza Bejo. (2014). Image Clustering Technique in Oil Palm Fresh Fruit Bunch (FFB) Growth Modeling, Agriculture and Agricultural Science Procedia, 2, 337-344.
Palacios-Morillo, A., Jurado, J. M., Alcázar, A., & Pablos, F. (2016). Differentiation of Spanish paprika from Protected Designation of Origin based on color measurements and pattern recognition. Food Control, 62, 243–249.
Pamornnak, B., Limsiroratana, S., Khaorapapong, T., Chongcheawchamnan, M., & Ruckelshausen, A. (2017). An automatic and rapid system for grading palm bunch using a Kinect camera. Computers and Electronics in Agriculture, 143, 227–237.
Pérez-Zavala, R., Torres-Torriti, M., Cheein, F. A., & Troni, G. (2018). A pattern recognition strategy for visual grape bunch detection in vineyards. Computers and Electronics in Agriculture, 151, 136–149.
Septiarini, A., Hamdani, H., Hatta, H. R., & Anwar, K. (2020). Automatic image segmentation of oil palm fruits by applying the contour-based approach. Scientia Horticulturae, 261, 108939.
Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A. A., Utaminingrum, F., & Hatta, H. R. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae, 286, 110245.
Setiawan, B. D., & Nur, Y. S. (2020). Motivasi, Permasalahan Dan Alternatif Solusi Program Siska (Sistem Integrasi Sapi Dan Kelapa Sawit) Di Kabupaten Pasaman Barat. Buletin Ilmiah Nagari Membangun, 3(2), 141–152. http://buletinnagari.lppm.unand.ac.id/index.php/bln/article/view/205
Sharif, Z. B. M., Taib, N. B. M., Yusof, M. S. Bin, Rahim, M. Z. Bin, Tobi, A. L. B. M., & Othman, M. S. Bin. (2017). Study on handing process and quality degradation of oil palm fresh fruit bunches (FFB). IOP Conference Series: Materials Science and Engineering, 203(1), 12027.
Shiddiq, M., Fitmawati, Anjasmara, R., Sari, N., & Hefniati. (2017). Ripeness detection simulation of oil palm fruit bunches using laser-based imaging system. AIP Conference Proceedings, 1801(1), 50003.
Sinambelaal, R., Mandang, T., Subrata, I. D. M., & Hermawan, W. (2020). Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm. Scientia Horticulturae, 265, 109231.
Suharjito, et al. (2021). "Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches." Computers and Electronics in Agriculture 188: 106359.
Sunil kumar, K., & Babu, D.S. (2013). Surface color based prediction of oil content in oil palm (Elaeis guineensis Jacq.) fresh fruit bunch. African Journal of Agricultural Research, 8, 564-569.
Tan, K., Lee, W. S., Gan, H., & Wang, S. (2018). Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosystems Engineering, 176, 59–72.
Tan, Y. A., Low, K. W., Lee, C. K., & Low, K. S. (2010). Imaging technique for quantification of oil palm fruit ripeness and oil content. European Journal of Lipid Science and Technology, 112(8), 838–843.
Taparugssanagorn, A., Siwamogsatham, S., & Pomalaza-Ráez, C. (2015). A non-destructive oil palm ripeness recognition system using relative entropy. Computers and Electronics in Agriculture, 118, 340–349.
Winata, H. N., Nasution, M. A., Ahamed, T., & Noguchi, R. (2021). Prediction of concentration for microalgae using image analysis. Multimedia Tools and Applications, 80(6), 8541–8561. https://doi.org/10.1007/s11042-020-10052-y
Winata, H. N., Noguchi, R., Tofael, A., & Nasution, M. A. (2019). Prediction of Microalgae Total Solid Concentration by Using Image Pattern Technique. Journal of the Japan Institute of Energy, 98(5), 73–84. https://doi.org/10.3775/jie.98.73