Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 28 | 3 |
Tytuł artykułu

Automatic non-destructive quality inspection system for oil palm fruits

Treść / Zawartość
Warianty tytułu
Języki publikacji
In this research a non-destructive, rapid and cost effective examination machine for the estimation of the ripeness fraction, oil content and free fatty acid level in oil palm fresh fruits bunch was developed. The automatic machine-vision based in- spection system provided consistency, rapid estimation and accep- table accuracy results in non-dest ructive manner. Fresh fruits bunch samples from Tenera cultivar (7 to 20 years trees) were taken from Cimulang plantation, Bogor, Indonesia. Two statistical analysis methods were used: a forward stepwise multiple linear regression analysis and a multilayer-perceptron artificial neural network analysis. The best prediction of ripeness and oil content models were obtained using the latter method, while the best free fatty acid prediction model was developed by the first method. The models were then employed in the machine-vision inspection systems of the machine. The system best prediction accuracy of ripeness, oil content and free fatty acid models was 93.5, 96.41, and 89.32%, with standard error of prediction being 0.065, 0.044 and 0.068, respectively. The system was tested through a series of field tests, and successfully examined more than 12 t of fruits bunch per hour, without causing damage.
Opis fizyczny
  • Agricultural Systems and Engineering, Asian Institute of Technology, Pathumthani 12120, Thailand
  • Department of Agricultural Engineering, Andalas University, West Sumatera 25163, Indonesia
  • School of Agricultural Technology, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki 57400, Greece
  • Agricultural Systems and Engineering, Asian Institute of Technology, Pathumthani 12120, Thailand
  • Kaziranga University, Jorhat, Assam, India
  • AOCS, 2004. Official methods and recommended practices of the American Oil Chemists Society. Sampling and analysis of commercial fats and oil. American Oil Chemists Society, Urbana, IL, USA.
  • APHA, 2005. Standard Methods for the Examination of Water and Wastewater, APHA, AWWA, WEF, part 5220 B. American Public Health Association, Washington DC, USA.
  • Arefi A., Motlagh A.M., and Teimourlou R.F., 2011.Wheat class identification using computer vision system and artificial neural networks. Int. Agrophys., 25, 319-325.
  • Arenas-Ocampo M., Alamilla-Beltrán L.,Vanegas-Espinoza P., Camacho-Díaz B.,Campos-Mendiola R., Gutiérrez-López G., and Jiménez-Aparicio A., 2012. Fractal morphology of Beta vulgaris L. cell suspension culture permeabilized with Triton X-100. Int. Agrophys., 26, 1-6.
  • Asadi V., Raoufat M., and Nassiri S., 2012. Fresh eggmass estimation using machine vision technique. Int. Agrophys., 26, 229-234.
  • Bobbio P.A., and Bobbio F.O., 2001. Chemical Food Processing (in Portuguese). Varela, Sao Paulo, Brazil.
  • Briseno-Tepepa B.R., Jiménez-Peréz J.L., Saavedra R., González- Ballesteros R., and Suaste E., Cruz-Orea A., 2008. Photopyroelectric microscopy of plant leaves. Int. J. Thermophysics, 29(6), 2200-2205.
  • Gonzalez R.C. and Woods R.E., 2008. Digital Image Processing. Pearson Prentice Hall, Pearson Education, Inc. New Jersey, USA.
  • Gonzalez-Ballesteros R., Gonzalez M.C.O., Suaste-Gomez E., and Cruz-Orea A., 2006. Polivinilidene fluoride (PVDF) applied to photopyroelectric microscopy. Proc. 3rd Int. Conf. Electrical and Electronics Engineering, September 6-8, Veracruz, Mexico.
  • Hadi S.,Ahmad D., and Akande F.B., 2009. Determination of the bruise indexes of oil palm fruits. J. Food Eng., 95, 322-326.
  • Hernandez A.C., Cruz O.A., Ivanov R., Dominguez P.A., Carballo C.A., and Moreno I., 2011. Optical properties of maize seeds. Int. Agrophys., 25, 223-227.
  • Hernandez A.C., Dominguez P.A., Cruz O.A., Ivanov R., Carballo C.A., and Zepeda B.R., 2010. Laser in agriculture. Int. Agrophys., 24, 407-422.
  • IOPRI, 1997. Palm oil and palm oil mill waste management. Team of standardization for palm oil processing. Indonesian Oil Palm Research Institute (IOPRI) (in Indonesian). Directorate general of forestry, Indonesia.
  • Jaffar A., Jaafar R., Jamil N., Low C. Y., and Abdullah B., 2009. Photogrammetric grading of oil palm fresh fruit bunches. Int. J. Mechanical Mechatronics Eng., 9(10), 7-13.
  • Jamil N., Mohamed A., and Abdullah S., 2009. Automated grading of palm oil fresh fruit bunches (FFB) using neuro-fuzzy technique. Int. Conf. Soft Computing and Pattern Recognition, 245-249.
  • Junkwon P., Takigawa T., Okamoto H., Hasegawa H., Koike M., Sakai K., Siruntawineti J., Chaeychomsri W., Vanavichit A., Tittinuchanon P., and Bahalayodhin B., 2009. Hyperspectral maging for nondestructive determination of internal qualities for oil palm (ElaeisguineensisJacq. var. tenera). Agric. Information Res., 18(3), 130-141.
  • Koenderink N.J.J.P., Broekstra J., and Top J.L., 2010. Bounded transparency for automated inspection in agriculture. Computers Electronics Agric., 72, 27-36.
  • Kondo N., 2010. Automation on fruit and vegetable grading system and food traceability. Trends in Food Sci. Technol., 21, 145-152.
  • Liu Y., Chen X., and Ouyang A., 2008. Nondestructive determination of pear internal quality indices by visible and nearinfrared spectrometry. LWT - Food Sci. Technol., 41, 1720-1725.
  • Makky M., Herodian S., and Subrata I.D.M., 2004. Design and Technical test of visual sensing system for palm oil harvesting robot. Proc. Int. Seminar on Advanced Agric. Eng. FarmWork Operation, August 25-26, Bogor, Indonesia.
  • Makky M. and Soni P., 2013a. Towards sustainable green production: exploring automated grading for oil palm fresh fruit bunches (FFB) using machine vision and spectral analysis. Int. J. Advanced Sci. Eng. Information Technol., 3(1), 1-7.
  • Makky M. and Soni P., 2013b. Development of an automatic grading machine for oil palm fresh fruit bunches (FFBs) based on machine vision. Computers Electronics Agric., 93, 129-139.
  • Makky M. and Soni P., 2014. In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach. J. Food Eng., 120, 248-259.
  • Matsushima U., Graf W., Zabler S., Manke I., Dawson M., Choinka G., Hilger A., and Herppich W., 2013. 3D-analysis of plant microstructures: advantages and limitations of synchrotron X-ray microtomography. Int. Agrophys., 27, 23-30.
  • Mireei S.A.,Mohtasebi S.S.,Massudi R.,Rafiee S., and Arabanian A.S., 2010. Feasibility of near infrared spectroscopy for analysis of date fruits. Int. Agrophys., 24, 351-356.
  • Naes T. and Mevik B.H., 2001. Understanding the collinearity problem in regression and discriminant analysis. J. Chemometrics, 15(4), 413-426.
  • Nicolai B.M., Beullens K., Bobelyn E., Peirs A., Saeys W., Theron K.I., and Lammertyn J., 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectro- scopy: A review. Postharvest Biol. Tech., 46, 99-118.
  • NSAI, 2006. Crude Palm Oil. National Standardization Body ofIndonesia (in Indonesian). SNI 01-2901-2006. Stipulation No. 107/KEP/BSN/05/2006, Indonesia.
  • Osawa C.C., Goncalves L.A.G., and Ragazzi S., 2007. Correlation between free fatty acids of vegetable oils evaluated by rapid tests and by the official method. J. Food Composition Analysis, 20, 523-528.
  • Razali M.H., Ismail W.I.W., Ramli A.R., Sulaiman N., and Harun M.H.B., 2011. Technique on simulation for real time oil palm fruits maturity prediction. African J. Agric. Res., 6(7), 1823-1830.
  • Roseleena J., Nursuriati J., Ahmed J., and Low C.Y., 2011. Assessment of palm oil fresh fruit bunches using photogrammetric grading system. Int. Food Res. J., 18(3), 999-1005.
  • Saad B., Ling C.W., Jab M.S., Lim B.P., Ali A.S.M., and Wai W.T., 2006. Determination of free fatty acids in palm oil samples using non-aqueous flow injection titrimetric method. Food Chemistry, 102, 1407-1414.
  • Shaarani S.M.D., Cardenas-Blanco A., Amin M.H.G., Soon N.G., andHallL.D., 2010. Monitoring Development and Ripeness of Oil Palm Fruit (Elaeis guneensis) by MRIand Bulk NMR. Int. J. Agric. Biol., 12(1), 101-105.
  • Shariff R., Nor A., Adnan R.M., Shattri M., Rohaya H., and Roop G., 2004. Correlation between oil content and dn values. University Putra Malaysia, Siregar I.M., 1976. Assessment of ripeness and crop control in oil palm. Proc. Malaysian Int. Agric. Oil Palm Conf.,June14-17, Kuala Lumpur, Malaysia.
  • Zapotoczny P., 2012. Application of image texture analysis for varietal classification of barley. Int. Agrophys., 26, 81-90.
  • Zdunek A., Adamiak A., Pieczywek P.M., and Kurenda A., 2014. The biospeckle method for the investigation of agricultural crops: A review. Optics Lasers Eng., 52, 276-285.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.