Image processing methods to evaluate tomato and zucchini damage in post-harvest stages
Keywords:
image processing, color space, smartphone, efficient stitching, homography, controlled supervision, artificial vision, embedded parallel processing, injury assessment, traceability, post-harvest control, feature detectionAbstract
Through the supply chain, the quality or quality change of the products can generate important losses. The quality control in some steps is made manually that supposes a high level of subjectivity, controlling the quality and its evolution using automatic systems can suppose a reduction of the losses. Testing some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this study. Two steps in the supply chain are considered, the feeding of the raw products into the handling chain (because low quality generates a reduction of the chain productivity) and the cool storage of the processed products (as the value at the market is reduced). It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products (corresponding to specific farmers/suppliers, it should be asked to improve to maintain the productivity of the line). The second stage is analyzing the evolution of the products along the cool chain (storage and transport), the use of an App developed to be use under Android was proposed to substitute the “visual” evaluation used in practice. The algorithms used, including stages of pre-treatment, segmentation, analysis and presentation of the results take account of the short time available and the limited capacity of the batteries. High performance techniques were applied to the homography stage to discard some of the images, resulting in better performance. Also threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the products. The proposed method achieves success rates comparable to, and improving, the expert inspection. Keywords: image processing, color space, smartphone, efficient stitching, homography, controlled supervision, artificial vision, embedded parallel processing, injury assessment, traceability, post-harvest control, feature detection DOI: 10.25165/j.ijabe.20171005.3087 Citation: Alvarez-Bermejo J A, Giagnocavo C, Li M, Morales C E, Santos D P M, Yang X T. Image processing methods to evaluate tomato and zucchini damage in post-harvest stages. Int J Agric & Biol Eng, 2017; 10(5): 126–133.References
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[2] Zhang B H, Huang W Q, Li J B, Zhang C J, Fan S X, Wu J T, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 2014; 62: 326–343
[3] Cubero S, Lee W S, Aleixos N, Albert F, Blasco J. Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest: A review. Food Bioproc Tech, 2016; 9: 1623–1639
[4] Rong D, Rao X Q, Ying Y B. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Comput Electron Agr, 2017; 137: 59–68
[5] Khoje S A, Bodhe S K. A Comprehensive survey of fruit grading systems for tropical fruits of maharashtra. Crit Rev Food Sci, 2015; 55(12): 1658–1671
[6] Su Q H, Kondo N, Li M Z, Sun H, Al Riza D F. Potato feature prediction based on machine vision and 3D model rebuilding. Comput Electron Agr, 2017; 137: 41–51
[7] Cubero S, Aleixos N, Albert A, Torregrosa A, Ortiz, C, García-Navarrete O, Blasco J. Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precis Agric, 2014; 15(1): 80–94.
[8] Wang L, Tian X, Li A, Li H. Machine vision applications in agricultural food logistics. Proceedings of 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013, art. no. 6961105, 2014; pp. 125–129.
[9] Brown M, Lowe D G. Automatic panoramic image stitching using invariant features. Int J Comput Vision, 2007; 74(1): 59–73.
[10] Panchal P M, Panchal S R, Shah S K. A Comparison of SIFT and SURF. International Journal of Innovative Research in Computer and Communication Engineering, 2013; 1(2): 323–327
[11] OpenGL-ARB. OpenGL Reference Manual: The Official Reference Document to OpenGL, Version 1.1. Addison-Wesley, Reading, MA, 2nd edition. 1997.
[12] Mikolajczyk K & Schmid C. Scale & affine invariant interest point detectors. Int J Comput Vision, 2004; 60(1): 63–86.
[13] Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, et al. A comparison of affine region detectors. Int J Comput Vision, 2005; 65(1/2): 43–72.
[14] McIlhagga W. The canny edge detector revisited. Int J Comput Vision, 2011; 91(3): 251–261.
[15] Xin G, Ke C, Hu X G. An improved Canny edge detection algorithm for color image. IEEE 10th International Conference on Industrial Informatics, Beijing, 2012, pp. 113–117.
[16] Wu C C, Zhou L, Wang J, Cai Y P. Smartphone based precise monitoring method for farm operation. Int J Agric & Biol Eng, 2016; 9(3): 111–121.
[17] Xiao B X, Wang C Y, Guo X Y, Wu S. Image acquisition system for agricultural context-aware computing. Int J Agric & Biol Eng, 2014; 7(4): 75–80.
[18] Nagle M, Intani K, Romano G, Mahayothee B, Sardsud V, Müller J. Determination of surface color of ‘all yellow’ mango cultivars using computer vision. Int J Agric & Biol Eng, 2016; 9(1): 42–50.
[19] Zhang C L, Zhang S W, Yang J C, Shi Y C, Chen J. Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int J Agric & Biol Eng, 2017; 10(2): 74–83.
[20] Choe Lip Haw, Wan Ishak Wan Ismail, Siti Kairunniza-Bejo, Adam Putih, Ramin Shamshiri. Colour vision to determine paddy maturity. Int J Agric & Biol Eng, 2014; 7(5): 55–63.
[21] Kim D G, Burks T F, Qin J W, Bulanon D M. Classification of grapefruit peel diseases using color texture feature analysis .Int J Agric & Biol Eng, 2009; 2(3): 41–50.
[22] Zhang J X, Ma Q Q, Li W, Xiao T T. Feature extraction of jujube fruit wrinkle based on the watershed segmentation. Int J Agric & Biol Eng, 2017; 10(4): 165–172.
[23] Sun G X, Li Y B, Wang X C, Hu G Y, Wang X, Zhang Y. Image segmentation algorithm for greenhouse cucumber canopy under various natural lighting conditions. Int J Agric & Biol Eng, 2016; 9(3): 130–138.
[24] Narendra V G, Hareesh K S. Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision. Int J Agric & Biol Eng, 2011; 4(2): 83–90.
[25] Aghdam M S, Bodbodak S. Postharvest Heat Treatment for Mitigation of Chilling Injury in Fruits and Vegetables. Food Bioproc Tech, 2014; 7(1): 37–53.
[26] Sevillano L, Sánchez-Ballesta M T, Romojaro F, Flores F B. Physiological, hormonal and molecular mechanisms regulating chilling injury in horticultural species. postharvest technologies applied to reduce its impact. J Sci Food Agr, 2009; 89(4): 555–573.
[27] Szeliski R. Image Alignment and Stitching: A Tutorial. Foundations and Trends in Computer Graphics and Vision, 2006; 2(1): 1–109.
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Published
2017-09-30
How to Cite
Álvarez-Bermejo, J. A., Giagnocavo, C., Ming, L., Morales, E. C., Santos, D. P. M., & Xinting, Y. (2017). Image processing methods to evaluate tomato and zucchini damage in post-harvest stages. International Journal of Agricultural and Biological Engineering, 10(5), 126–133. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3087
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Information Technology, Sensors and Control Systems
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