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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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):29–39
Sharma R, Kaur M (2015) ANN based technique for vegetable quality detection. IOSR J Electron Commun Eng 10(5):62–70
Ramprabhu J, Nandhini S (2014) Enhanced technique for sorting and grading the fruit quality using MSP430 controller. Int J Adv Eng Technol 7(5):1483–1488
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 10:62–68
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 598–603
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 541–546
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, Singapore
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 4802–4805
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 391–396
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):1721–1730
Ukirade NS (2014) Color grading system for evaluating tomato maturity. Int J Res Manage Sci Technol 2(1):41–45
Saito Y, Hatanaka T, Uosaki K (2003) Eggplant classification using artificial neural network. In: Proceedings of the international joint conference, Portland, pp 1013–1018
Calpe J, Pla F, Monfort J (2002) Robust low-cost vision system for fruit grading. In: Proceedings of electrotechnical conference, Bari, pp 1710–1713
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 194–199
Pavithra V, Pounroja R, SathyaBama B (2015) Machine vision based automatic sorting of cherry tomatoes. In: Proceedings of electronics and communication systems, Coimbatore, pp 271–275
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):45–56
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 214–220
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):292–302
Mizushima A, Lu R (2013) An image segmentation method for apple sorting and grading. Comput Electron Agric 94:29–37
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, S., Girdhar, A., Gill, J. (2018). Computer Vision-Based Tomato Grading and Sorting. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 38. Springer, Singapore. https://doi.org/10.1007/978-981-10-8360-0_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-8360-0_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8359-4
Online ISBN: 978-981-10-8360-0
eBook Packages: EngineeringEngineering (R0)