Advertisement

Computer Vision-Based Tomato Grading and Sorting

  • Sukhpreet Kaur
  • Akshay Girdhar
  • Jasmeen Gill
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 38)

Abstract

Since ages, agricultural sector plays an important role in the economic development of a country. In recent years, industries have started using automated systems instead of manual techniques for quality evaluation. In agriculture field, grading is very necessary to increase the productivity of the vegetable products. Everyday a huge amount of vegetables are exported to other places and earn a good profit. So, quality evaluation is important in terms of improving the quality of vegetables and gaining profit. Traditionally, the vegetable grading and classification were done through manual procedures which were error prone and costly. Computer vision-based systems provide us such accurate and reliable results that are not possible with human graders/experts. This paper presents a vegetable grading and sorting system based on computer vision and image processing. For this work, tomatoes have been used as a sample vegetable. A total of 53 images were acquired using own camera setup. Afterward, segmentation using Otsu’s method was performed so as to separate the vegetable from the background. The segmented images, thus obtained, were used to extract color and shape features. At last, grading and sorting were performed using backpropagation neural network. The proposed method has shown an accuracy of 92% and outperformed the existing system.

Keywords

Computer vision Image processing Grading and sorting Feature extraction Artificial neural network 

References

  1. 1.
    Pandey R, Naik S, Marfatia R (2013) Image processing and machine learning for automated fruit grading system: a technical review. Int J Comput Appl 81(16):29–39Google Scholar
  2. 2.
    Sharma R, Kaur M (2015) ANN based technique for vegetable quality detection. IOSR J Electron Commun Eng 10(5):62–70Google Scholar
  3. 3.
    Ramprabhu J, Nandhini S (2014) Enhanced technique for sorting and grading the fruit quality using MSP430 controller. Int J Adv Eng Technol 7(5):1483–1488Google Scholar
  4. 4.
    Danti A, Madgi M, Anami BS (2014) A neural network based recognition and classification of commonly used indian non-leafy vegetables. Int J Image, Graph Signal Process 10:62–68CrossRefGoogle Scholar
  5. 5.
    Kumar A, Gill GS (2015) Automatic fruit grading and classification system using computer vision: a review. In: Proceedings of advances in computing and communication engineering, Dehradun, pp 598–603Google Scholar
  6. 6.
    Pla F, Sanchiz JM, Sanchez JS (2001) An integral automation of industrial fruit and vegetable sorting by machine vision. In: Proceedings of emerging technologies and factory automation, France, pp 541–546Google Scholar
  7. 7.
    Chalidabhongse T, Yimyam P, Sirisomboon P (2006) 2D/3D vision-based mango’s feature extraction and sorting. In: Proceedings of the international conference on control, automation, robotics and vision, SingaporeGoogle Scholar
  8. 8.
    Feng G, Qixin C (2004) Study on color image processing based intelligent fruit sorting system. In: Proceedings of international conference on intelligent control and automation, China, pp 4802–4805Google Scholar
  9. 9.
    Mustafa NBA, Ahmed SK, Ali Z (2009) Agricultural produce sorting and grading using support vector machines and fuzzy logic. In: Proceedings of international conference on signal and image processing applications, Malaysia, pp 391–396Google Scholar
  10. 10.
    Nandi CS, Tudu B, Koley C (2014) A machine vision-based maturity prediction system for sorting of harvested mangoes. IEEE Trans Instrum Measure 62(7):1721–1730CrossRefGoogle Scholar
  11. 11.
    Ukirade NS (2014) Color grading system for evaluating tomato maturity. Int J Res Manage Sci Technol 2(1):41–45Google Scholar
  12. 12.
    Saito Y, Hatanaka T, Uosaki K (2003) Eggplant classification using artificial neural network. In: Proceedings of the international joint conference, Portland, pp 1013–1018Google Scholar
  13. 13.
    Calpe J, Pla F, Monfort J (2002) Robust low-cost vision system for fruit grading. In: Proceedings of electrotechnical conference, Bari, pp 1710–1713Google Scholar
  14. 14.
    Lee DJ, Chang Y, Archibald JK (2008) Color quantization and image analysis for automated fruit quality evaluation. In: Proceedings of international conference on automation science and engineering, Arlington, pp 194–199Google Scholar
  15. 15.
    Pavithra V, Pounroja R, SathyaBama B (2015) Machine vision based automatic sorting of cherry tomatoes. In: Proceedings of electronics and communication systems, Coimbatore, pp 271–275Google Scholar
  16. 16.
    Bhatt AK (2013) Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI Soc 30(1):45–56MathSciNetCrossRefGoogle Scholar
  17. 17.
    Mhaski RR, Chopade PB, Dale MP (2015) Determination of ripeness and grading of tomato using image analysis on Raspberry Pi. In: Proceedings of international conference on communication, control and intelligent systems, Mathura, pp 214–220Google Scholar
  18. 18.
    Lee DJ, Archibald JK, Xiong G (2011) Rapid color grading for fruit quality evaluation using direct color mapping. IEEE Trans Autom Sci Eng 8(2):292–302CrossRefGoogle Scholar
  19. 19.
    Mizushima A, Lu R (2013) An image segmentation method for apple sorting and grading. Comput Electron Agric 94:29–37CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Guru Nanak Dev Engineering CollegeLudhianaIndia
  2. 2.RIMT IETMandi GobindgarhIndia

Personalised recommendations