Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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  • SJR 0.293

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Indian Journal of Agricultural Research, volume 54 issue 6 (december 2020) : 716-723

Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN)

Dasharathraj K. Shetty, U. Dinesh Acharya, V.G. Narendra, P.J. Prajual
1Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal- 576 104, Karnataka, India.
Cite article:- Shetty K. Dasharathraj, Acharya Dinesh U., Narendra V.G., Prajual P.J. (2020). Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN). Indian Journal of Agricultural Research. 54(6): 716-723. doi: 10.18805/IJARe.A-5374.
Background: In this digital era, agricultural science can significantly benefit from automation. More specifically, automated processes can be used to evaluate the quality of various fruits and vegetables. The main selling point of fruits and vegetables is their sensory characteristics, i.e., their appearance and smell as they significantly impact consumer choice and market value. Sorting and grading of agricultural products are carried out manually, which lead to inconsistency in results; besides, it can also be time-consuming, variable, subjective, onerous, expensive and prone to be influenced by surroundings. These factors build the case for an intelligent automated system to grade fruits and vegetables. 
Methods: This paper proposes an intelligent automated system that uses, Image Processing Techniques and Artificial Neural Network (ANN), to grade Dry Red Chillies(DRC). 
Result: A HSV based algorithm with the efficiency of 80.5 was developed and used to measure the various morphological characteristics of DRC and then clustered into various grades using ANN - Self-Organizing Map (SOM).
  1. Al Ohali, Y. (2011). Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University -Computer and Information Sciences. 23(1): 29-36.
  2. Blasco, J., Aleixos, N. and Molto, E. (2007). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering. 81(3): 535-543.
  3. Bhargava, A. and Bansal, A. (2018). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University-Computer and Information Sciences.
  4. Bouganis, A. and Shanahan, M. (2007). A vision-based intelligent system for packing 2-D irregular shapes. IEEE Transactions on Automation Science and Engineering. 4(3): 382-394.
  5. Chopde, S., Patil, M., Shaikh, A., Chavhan, B. and Deshmukh, M. (2017). Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables -A review. Agricultural Reviews. 38(2): 94-102.
  6. Department of Agriculture and Cooperation. (2009). Post-harvest Profile of Chilli. Nagpur: Government of India Ministry of Agriculture.
  7. Fudholi, A., Othman, M.Y., Ruslan, M.H. and Sopian, K. (2013). Drying of Malaysian Capsicum annuum L. (red chili) dried by open and solar drying. International Journal of Photoenergy. 2013. Pp 9 
  8. Garcia, H.C. and Villalobos, J.R. (2009). Automated refinement of automated visual inspection algorithms. IEEE Transactions on Automation Science and Engineering. 6(3): 514-524.
  9. Guil-Guerrero, J.L., Martínez-Guirado, C., Del Mar Rebolloso-Fuentes, M. and Carrique-Pérez, A. (2006). Nutrient composition and antioxidant activity of 10 pepper (Capsicum annuun) varieties. European Food Research and Technology. 224(1): 1-9.
  10. Karpate, R.R. andSaxena, R. (2009). Post-harvest profile of chilli. Dept. Ag. Cooperation, Ministry of Agriculture, Nagpur, India.
  11. Narendra, V.G. and Hareesha, K.S. (2010a). Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. International Journal of Computer Applications. 1(4): 1-9.
  12. Narendra, V.G. andHareesha, K.S. (2010b). Quality inspection and grading of agricultural and food products by computer vision-A review. International Journal of Computer Applications. 2(1): 43-65.
  13. Narendra, V.G., Shetty, D.K. andHareesh, K.S. (2012). Computer Vision system for cashew kernel area estimation. In: 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT’12) (pp. 1-6). IEEE.
  14. Nandi, C.S., Tudu, B. and Koley, C. (2014). Machine vision based techniques for automatic mango fruit sorting and grading based on maturity level and size. In: Sensing Technology: Current Status and Future Trends II, Springer, Cham. (pp. 27-46).
  15. Shearer, S.A. and Payne, F.A. (1990). Color and defect sorting of bell peppers using machine vision. Transactions of the ASAE, 33(6): 1245-1250.
  16. Simonne, A.H., Simonne, E.H., Eitenmiller, R.R., Mills, H.A. and Green, N.R. (1997). Ascorbic acid and provitamin a contents in unusually colored bell peppers (Capsicum annuum L.). Journal of Food Composition and Analysis. 10(4): 299-311.
  17. Sun, D.W. (Ed.). (2016). Computer Vision Technology for Food Quality Evaluation. Academic Press.
  18. Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F. and Debeir, O. (2011). Automatic grading of Bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture. 75(1): 204-212.

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