DETECTION OF LOTUS ROOT CONTAMINANTS USING INTELLIGENT VISUAL MACHINE VISION TECHNIQUES

Yuan Hao, Samuel Britwum Wilson, Emmanuel Asamoah, Jianrong Cia, Xukang Bao

Abstract


Lotus root, which is a water plant cherished by people in the Asian continent and some other parts of the world, is manually inspected for quality by experts to detect impurities. There is the need to update this inspection process in order to improve the quality and safety of lotus root. Machine vision systems and techniques are used for consistent, efficient, effective, and reliable inspection of images. The lotus root inspection system has been proposed to inspect the lotus roots for impurities. The detection algorithms use the size, shape, texture and color of the lotus root images as parameters to analyze the quality of lotus roots. The lotus root undergoes some processes before image acquisition and image processing. The camera and illumination used, in collaboration with the edge detection, and image segmentation techniques, efficiently and effectively exposed the impurities in the lotus root at a much faster rate. Also, it is less expensive compared to the traditional human inspections.


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References


Alçiçek, Z., & Balaban, M. Ö. (2012). Development and application of “The Two Image” method for accurate object recognition and color analysis. Journal of Food Engineering, 111(1), 46-51.

Ali, J. M., Jailani, H. S., & Murugan, M. (2019). Surface roughness evaluation of milled surfaces by image processing of speckle and white-light images Advances in manufacturing processes (pp. 141-151): Springer.

Ali, S., Khan, A. S., Anjum, M. A., Nawaz, A., Naz, S., Ejaz, S., & Hussain, S. (2020). Effect of postharvest oxalic acid application on enzymatic browning and quality of lotus (Nelumbo nuciferaGaertn.) root slices. Food Chemistry, 312, 126051. doi:https://doi.org/10.1016/j.foodchem.2019.126051

Dilpreet, K., & Kaur, Y. (2014). Various image segmentation techniques: a review. International Journal of Computer Science and Mobile Computing, 3(5), 809-814.

Gao, H., Chai, H., Cheng, N., & Cao, W. (2017). Effects of 24-epibrassinolide on enzymatic browning and antioxidant activity of fresh-cut lotus root slices. Food Chemistry, 217, 45-51. doi:10.1016/j.foodchem.2016.08.063

Gao, R., & Wu, H. (2015). Agricultural image target segmentation based on fuzzy set. Optik, 126(24), 5320-5324.

Garcia, S. N., Osburn, B. I., & Jay-Russell, M. T. (2020). One Health for Food Safety, Food Security, and Sustainable Food Production. Frontiers in Sustainable Food Systems, 4(1). doi:10.3389/fsufs.2020.00001

González-Rivas, F., Ripolles-Avila, C., Fontecha-Umaña, F., Ríos-Castillo, A. G., & Rodríguez-Jerez, J. J. (2018). Biofilms in the Spotlight: Detection, Quantification, and Removal Methods. Comprehensive Reviews in Food Science and Food Safety, 17(5), 1261-1276. doi:10.1111/1541-4337.12378

Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB: Pearson Education India.

Iraji, M. S. (2019). Comparison between soft computing methods for tomato quality grading using machine vision. Journal of Food Measurement and Characterization, 13(1), 1-15. doi:10.1007/s11694-018-9913-2

Jothiaruna, N., Joseph Abraham Sundar, K., & Karthikeyan, B. (2019). A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing. Computers and Electronics in Agriculture, 165, 104934. doi:https://doi.org/10.1016/j.compag.2019.104934

Jyothi, S., & K.Bhargavi. (2014). A Survey on Threshold Based Segmentation Technique in Image Processing. 26. K. Bhargavi, S. Jyothi, 3.

Kneip, J., Fleischmann, P., & Berns, K. (2020). Crop edge detection based on stereo vision. Robotics and Autonomous Systems, 123, 103323. doi:https://doi.org/10.1016/j.robot.2019.103323

Kumar, Y., Dubey, A. K., & Jothi, A. (2017, 5-6 May 2017). Pest detection using adaptive thresholding. Paper presented at the 2017 International Conference on Computing, Communication and Automation (ICCCA).

Li, D., Xu, L., & Liu, H. (2017). Detection of uneaten fish food pellets in underwater images for aquaculture. Aquacultural Engineering, 78, 85-94. doi:https://doi.org/10.1016/j.aquaeng.2017.05.001

Li, J., Bai, J., Li, S., Zhu, Z., Yi, Y., Wang, H., & Lamikanra, O. (2020). Effect of lactic acid bacteria on the postharvest properties of fresh lotus root. Postharvest Biology and Technology, 160, 110983. doi:https://doi.org/10.1016/j.postharvbio.2019.110983

Li, Y., Sun, R., Liu, Y., Yang, Y., Ma, S., & Liu, Y. (2019). Interactive foreground segmentation and shape reconstruction from RGBD images. Computers & Electrical Engineering, 79, 106463. doi:https://doi.org/10.1016/j.compeleceng.2019.106463

Mavridou, E., Vrochidou, E., Papakostas, G. A., Pachidis, T., & Kaburlasos, V. G. (2019). Machine Vision Systems in Precision Agriculture for Crop Farming. Journal of Imaging, 5(12), 89.

Meng, Y., Zhang, Z., Yin, H., & Ma, T. (2018). Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron, 106, 34-41. doi:https://doi.org/10.1016/j.micron.2017.12.002

Merzougui, M., & Allaoui, A. E. (2019). Region growing segmentation optimized by evolutionary approach and Maximum Entropy. Procedia Computer Science, 151, 1046-1051. doi:https://doi.org/10.1016/j.procs.2019.04.148

Mirante, E., Georgiev, M., & Gotchev, A. (2011, 16-18 May 2011). A fast image segmentation algorithm using color and depth map. Paper presented at the 2011 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON).

Momin, M. A., Rahman, M. T., Sultana, M. S., Igathinathane, C., Ziauddin, A. T. M., & Grift, T. E. (2017). Geometry-based mass grading of mango fruits using image processing. Information Processing in Agriculture, 4(2), 150-160. doi:https://doi.org/10.1016/j.inpa.2017.03.003

Park, J. J., & Lee, W. Y. (2020). Softening of lotus root and carrot using freeze-thaw enzyme infusion for texture-modified foods. Food Bioscience, 35, 100557. doi:https://doi.org/10.1016/j.fbio.2020.100557

Ramirez-Paredes, J.-P., & Hernandez-Belmonte, U.-H. (2019). Visual quality assessment of malting barley using color, shape and texture descriptors. Computers and Electronics in Agriculture, 105110. doi:https://doi.org/10.1016/j.compag.2019.105110

Rogowska, J. (2000). Overview and fundamentals of medical image segmentation. Handbook of medical imaging, processing and analysis, 69-85.

Roy, K., Chaudhuri, S. S., Bhattacharjee, S., Manna, S., & Chakraborty, T. (2019, 18-20 March 2019). Segmentation Techniques for Rotten Fruit detection. Paper presented at the 2019 International Conference on Opto-Electronics and Applied Optics (Optronix).

Ruedt, C., Gibis, M., & Weiss, J. (2020). Quantification of surface iridescence in meat products by digital image analysis. Meat Science, 163, 108064. doi:https://doi.org/10.1016/j.meatsci.2020.108064

Shafait, F., Keysers, D., & Breuel, T. (2008). Efficient implementation of local adaptive thresholding techniques using integral images (Vol. 6815): SPIE.

Shao, D., Xu, C., Xiang, Y., Gui, P., Zhu, X., Zhang, C., & Yu, Z. (2019). Ultrasound image segmentation with multilevel threshold based on differential search algorithm. IET Image Processing, 13(6), 998-1005.

Shrivakshan, G., & Chandrasekar, C. (2012). A comparison of various edge detection techniques used in image processing. International Journal of Computer Science Issues (IJCSI), 9(5), 269.

Sikora, T., & Strada, A. (2005). Safety and quality assurance and management systems in food industry: An overview. The Food Industry in Europe, Agricultural University of Athens, Ateny.

Vafeiadis, T., Dimitriou, N., Ioannidis, D., Wotherspoon, T., Tinker, G., & Tzovaras, D. (2018). A framework for inspection of dies attachment on PCB utilizing machine learning techniques. Journal of Management Analytics, 5(2), 81-94. doi:10.1080/23270012.2018.1434425

Vision, M. (2019). Improved Automatic Quality Inspections Through the Integration of State-of-the-Art Machine Vision and Collaborative Robots. Paper presented at the Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10-12 September 2019, Queen's University, Belfast.

Vitzrabin, E., & Edan, Y. (2016). Adaptive thresholding with fusion using a RGBD sensor for red sweet-pepper detection. Biosystems Engineering, 146, 45-56. doi:https://doi.org/10.1016/j.biosystemseng.2015.12.002

Wang, J., He, J., Han, Y., Ouyang, C., & Li, D. (2013). An Adaptive Thresholding algorithm of field leaf image. Comput. Electron. Agric., 96, 23–39. doi:10.1016/j.compag.2013.04.014

Zemmour, E., Kurtser, P., & Edan, Y. (2019). Automatic parameter tuning for adaptive thresholding in fruit detection. Sensors, 19(9), 2130.




DOI: https://doi.org/10.5296/jfi.v5i1.17813

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