Development of a Computer Vision System for Brown Rice Quality Analysis

Andie M. Tuates, Aileen R Ligisan

Abstract


Conventional brown rice analysis is done by visually inspecting each grain and classifying according to their respective categories.  This method is subjective and tedious leading to errors in analysis.  Computer vision could be used to analyze brown rice quality by developing models that correlate shape and color features with various classification.  The objective of the study was to develop a computer vision system (CVS) for predicting quality parameters of brown rice.Brown rice training samples were collected in Nueva Vizcaya, NFA Binalonan, Pangasinan, and SM supermarket. An ordinary flat bed scanner was used as image acquisition device coupled to a laptop computer equipped with image processing and analysis software developed at PHilMech. The CVS set-up was tested using samples collected at the regional NFA warehouses. The performance of the CVS was compared to human inspection based on their capability to classify brown rice samples.

An artificial neural network using probabilistic neural network (PNN) model was developed. Sensitivity analysis revealed a true positive proportion ranging from 0.8792 to 1.00. Likewise, a weight prediction model based on the projected area was made using linear regression. The developed equation is y = 0.00148A – 0.00018 with a R2 of 0.854.

The results of performance testing revealed that the CVS could predict the weight of brown rice and detect color-related quality of brown rice such as: sound, damaged, chalky/immature, yellow fermented, red, and paddy. Processing time for classification using the developed CVS has an average of 18.53 minutes and sixty percent of its time (equivalent to 11.24 minutes) was consumed in the manual arranging of grain samples. If a digital separation could be developed, the total time can be reduced to 7.11 minutes compared to 40.07 minutes of manual assessment. Moreover, CVS classification is more accurate compared with the human inspection.


Keywords


brown rice, computer vision system, human inspection, accuracy, repeatability

Full Text:

PDF

References


• Brosnan T., and Sun D.W. 2004. Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering. 61 (2004) 3–16.

• Bulaong M.C., Agustin O.C., Carbonel J. and Manalabe R.E. 2009. Quality analysis of milled rice using computer vision. Paper presented in the 21st National Research Symposium. Oct. 7-9, 2009. Bureau of Agricultural Research.

• Gunasekaran S. 1996. Computer vision technology for food quality assurance. Trends in Food Science and Technology, 7(8), 245–256.

• Hush D.R. and Horne B.G. 1993. Progress in supervised neural networks, what’s new since Lippmann. IEEE Signal Processing Magazine 10(1): 8–39.

• Luo X., Jayas D.S., Symons S.J. 1999. Comparison of statistical and neural network methods for classifying cereals grains using machine vision. Transactions of the ASAE, 42(2),413–419.

• Majumdar S., & Jayas D.S. 2000a. Classification of cereal grains using machine vision: I. Morphology models. Transactions of the ASAE, 43(6), 1669–1675.

• Majumdar S., & Jayas D.S. 2000b. Classification of cereal grains using machine vision: II. Color models. Transactions of the ASAE, 43(6), 1677–1680.

• Majumdar S., & Jayas D.S. 2000c. Classification of cereal grains using machine vision: III. Texture models. Transactions of the ASAE, 43(6), 1681–1687.

• Majumdar S., & Jayas D.S. 2000d. Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models. Transactions of the ASAE, 43(6), 1689–1694.

• Neuroshell 2 Release 4 Artificial Neural Network by Ward Systems Group Inc., Executive Park, West 5 Hilcrest Drive, Frederick MD, USA. Website: www.wardsystems.com.

• Paliwal J., Borhan M.S., and Jayas D.S. 2003. Classification of cereal grains using a flatbed scanner. In 2003 ASAE Annual International Meeting, Paper No. 036103. St. Joseph, Michigan, USA: ASAE.

• Paliwal J., Jayas D.S., Visen N.S., and White N.D.G. 2004. Feasibility of a machine-vision-based grain cleaner. Applied Engineering in Agriculture. 20(2):245-248.

• Philippine Agricultural Engineering Standards. 2001. Vol. 1. Agricultural Machinery Testing and Evaluation Center, College of Engineering and Agro-Industrial Technology, University of the Philippines, Los Banos, Laguna, Philippines.

• Roush W.B., Cravener T.L., Kochera Kirby Y., and Wideman Jr R. F. 1997. Probabilistic Neural Network Prediction of Ascites in Broilers Based on Minimally Invasive Physiological Factors. Department of Poultry Science, Pennsylvania State University, University Park, Pennsylvania, and Department of Poultry Science, University of Arkansas, Fayetteville, Arkansas.

• Shahin M., Symons S., and Meng A.. 2004. Seed sizing with image analysis. ASAE Meeting Paper No. 043121. St. Joseph, Mich.: ASAE

• Steenhoek L.W., Misra M.K., Hurburgh Jr., C.R. & Bern C.J. 2001a. Implementing a computer vision system for corn kernel damage. Applied Engineering in Agriculture. 17(2): 235–240.

• Test Data Bulletin for Rice Mill (1998-2004). Agricultural Machinery Testing and Evaluation Center (AMTEC), College of Engineering and Agro-Industrial Technology, University of the Philippines, Los Banos, Laguna, Philippines.

• Wang, H.-H. & D.W. Sun. 2001. Evaluation of the functional properties of cheddar cheese using a computer vision method. Journal of Food Engineering, 49(1), 47–51.

• Yun H.S., Lee W.O., Chung H., Lee H.D., Son J.R., Cho K.H., Park W.K. 2002. A computer vision system for rice kernel quality evaluation. ASAE Meeting Paper No. 023130. St. Joseph, Mich.: ASAE


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.