Inspection and Grading of Dried Coconut – an Automatic Method Using SVM Classification
Keywords:Copra, Grading, Image Processing, Segmentation, Classification
Coconut production and consumption rate of India being very high, contributes significantly towards the Indian economy; thus, ensuring the quality of allied products proves to be a necessity. Intelligent quality evaluation and grading system for copra images is proposed to classify copra, the dried coconut kernel into various categories using an image processing approach. The proposed method is useful in the current scenario, as quality deterioration during drying and storing of copra is a challenging task. Inferior quality of copra due to the presence of moisture, mold, fungi, bacteria and sulphur may adversely affect the shelf life thus deteriorating the quality of final products and human health. The grading system automatically segments the region of interest of copra images using SUSAN filter and classifies them into usable and unusable categories. The unusable categories of copra considered in the proposed method include copra with moisture, mold, wrinkles and sulphur. Various features of copra images were collected and analyzed. The selected features of copra images were used for training and classification using SVM classifier. The proposed method has been evaluated using a real database and the results are promising.
Commission of agriculture costs and prices, Department of Agriculture, Cooperation & Farmers Welfare Ministry of Agricul-ture & Farmers Welfare Government of India. 2020.
R. Jnanadevan, “Role of Coconut Development Board in entrepreneurship development and Value Addition”,” Indian Co-conut Journal, 2019.
J. M. N. Marikkar, M. K. I. Banu and C. Yalegama, “Evaluation of the modified-ceylon copra kiln for accelerated production of ball copra”,” International Food Research Journal, vol. 16, pp. 175–181, 2009.
P. Malabrigo, “Drying, storage, and preparation of copra for extraction of oil”,” J Am Oil Chem Soc, vol. 54, pp. 485–488, 1977.
A. S. Sagayaraj, G. Ramya and N. Dhanaraj, “Analysis of sulphur content in copra”,” ICTACT Journal on Image and Video Processing, vol. 9, no. 2, pp. 1882–1886, 2018.
A. Bhargava and A. Bansal, “Fruits and Vegetables Quality Evaluation Using Computer Vision: A Review”,” Journal of King Saud University - Computer and Information Sciences, vol. 33, pp. 243–257, 2018.
S. Sagayaraj and T. K. K. Devi, “Characteristic Recognition of copra using Image processing”,” International Journal of Engi-neering Science Invention Research & Development, vol. 2, pp. 272–279, 2015.
A. S. Sagayara, R. Pradeepa, P. Suganthi, S. Vinothini and T. Vishvapriya, “Vision based Features in Moisture Content Measurement-A Survey”,” International Journal in Advanced Research in Electrical, Electronics and Instrumentation Engi-neering, vol. 8, pp. 392–396, 2019.
R. Mahendran, G. Jayashree and C. K., Alagusundaram, “Application of Computer Vision Technique on Sorting and Grad-ing of Fruits and Vegetables”,” Journal of Food Processing Technology, vol. 24, no. 10, pp. 1–7, 2019.
K. K. Patel, M. A. Khan, A. Kar, Y. Kumar, L. M. Bal and D. K. Sharma, “Image Processing Tools and Techniques Used in Computer Vision for Quality Assessment of Food Products: A Review”,” International Journal of Food Quality and Safety, vol. 1, pp. 1–16, 2015.
K. K. Patel, A. Kar, S. N. Jha and M. A. Khan, “Machine vision system: a tool for quality inspection of food and agricultural products”,” Journal of Food Science Technology, vol. 49, pp. 123–141, 2012.
S. J. Rupali and S. S. Patil, “A Fruit Quality Management System Based On Image Processing”,” IOSR Journal of Electronics and Communication Engineering, vol. 8, pp. 1–5, 2013.
S. K. Sahoo, S. Pine, S. K. Mohapatra and B. B. Choudhury, “An effective quality inspection system using image processing techniques”,” in International Conference on Communications and Signal Processing, pp. 1426–1430, 2015.
S. S. Turgut and E. Karacabey, “Potential of Image Analysis based Systems in Food Quality Assessments and Classifica-tions”,” in The 9th Baltic Conference on Food Science and Technology, Food for consumer well-being, pp. 8–12, 2014.
W. Burger and M. J. Burge, “Edge-preserving smoothing filters,” in Texts in Computer Science, London: Springer London, pp. 413–451, 2016.
N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Trans Sys, Vol. 9, no. 1, pp. 62–66, 1979.
Y. Kimori, “Morphological image processing for quantitative shape analysis of biomedical structures: effective contrast enhancement”,” Journal of synchrotron radiation, vol. 20, pp. 848–853, 2013.
S.M. Smith and J.M. Brady, “SUSAN - A new approach to low level image processing,” International Journal of Computer Vision, vol. 23, no. 1, pp. 45–78, 1997.
V. N. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998.
S. N. Sivanandam, and S. N. Deepa, Introduction to Neural Networks using MATLAB 6.0. Computer Engineering Series. Tata Mc Graw Hill Education, 2006.
H. Bisgin, T. Bera, H. Ding, Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles. Scientific Reports, London vol. 8. pp. 1-12, 2018.
How to Cite
Copyright (c) 2023 Rekha Lakshmanan, Nirmal Dorothy, Abid Rahman, Ajmal
This work is licensed under a Creative Commons Attribution 4.0 International License.