Development of a Computer Vision System for Brown Rice Quality Analysis
Keywords:
brown rice, computer vision system, human inspection, accuracy, repeatabilityAbstract
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.
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
Downloads
Published
Issue
Section
License
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NonCommercial 4.0 International that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.