Abstract
India is among the top producers of rice in the world. It is the staple food for the eastern and southern people of India. Therefore, the need to measure the quality of rice has become a necessity, but there are still a limited number of viable and safe options that can be used for the grading of the rice grains. This paper solves the prior problem of quality assessment using the reliable method of image processing. This technique allows us to get an idea of the dimensions of the rice grains and accordingly grade them. Previously, there have been numerous researches in the field of rice grain quality assessment taking chalky, the opaque white part of the grain into consideration. It is one of the most important characteristics while analyzing and grading the rice grain. This paper calculates the dimensions, classifies the grains into different quality grades and checks for chalkiness.
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
Similar content being viewed by others
References
M.Z. Abdullah, A.S. Fathinul-Syahir, B.M.N. Mohd-Azemi, Automated inspection system for colour and shape grading of starfruit (Averrhoa carambola L.) using machine vision sensor. Trans. Inst. Meas. Control 27(2), 65–87 (2005)
B.S. Anami, V. Burkpalli, S.A. Angadi, N.M. Patil, Neural network approach for grain classification and gradation, in Proceedings of the Second National Conference on Document Analysis and Recognition, pp. 394–408 (2003)
V. Patil, V.S. Malemath, Quality analysis and grading of rice grain images. IJIRCCE 3(6), 5672–5678 (2015)
G. Kaur, B. Verma, Measurement standards based grading of rice kernels by separating touching kernels for embedded imaging applications. Int. J. Electron. Commun. Instrum. Eng. Res. Dev. (IJECIERD) 3, 127–134 (2013)
P. Neelamegam, S. Abirami, K.V. Priya, S. RubalyaValantina, Analysis of rice granules using image processing and neural network, in Conference on Information & Communication Technologies (ICT), pp. 879–884 (2013)
C.V. Maheshwari, K.R. Jain, C.K. Modi, Non-destructive quality analysis of Indian Basmati Oryza sativa ssp. Indica (Rice) using image processing, in International Conference on Communication Systems and Network Technologies (CSNT), pp. 189–193 (2012)
N.K. Jain, S.O. Khanna, K.R. Jain, Development of a classification system for quality evaluation of Oryza sativa L. (Rice) using computer vision, in International Conference on Communication Systems and Network Technologies (CSNT), pp. 1088–1092 (2014)
J.S. Aulakh, V.K. Banga, Grading of rice grains by image processing. IJERT 1(4), 1–4 (2012)
C.V. Maheshwari, K.R. Jain, Parametric quality analysis of Indian Ponia Oryza sativa ssp. Indica (rice). Int. J. Sci. Res. Dev. (IJSRD) (2013). ISSN:2321-0613
B. Verma, Image processing techniques for grading & classification of rice, in International Conference on Computer and Communication Technology (ICCCT), pp. 220–223 (2010)
L. Guang-rong, Rice color inspection based on image processing technique, in International Conference on Advances in Energy Engineering (ICAEE), pp. 134–137 (2010)
Y. Hu, Y. Du, L. San, J. Tian, Research on rice grain shape detection method based on machine vision, in International Conference on Control, Automation and Robotics (ICCAR), pp. 300–304 (2019)
T.G. Devi, P. Neelamegam, S. Sudha, Machine vision based quality analysis of rice grains, in International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 1052–1055 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, J.P., Pradhan, C. (2021). A Semi-automated Smart Image Processing Technique for Rice Grain Quality Analysis. In: Bhoi, A.K., Mallick, P.K., Balas, V.E., Mishra, B.S.P. (eds) Advances in Systems, Control and Automations . ETAEERE 2020. Lecture Notes in Electrical Engineering, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-15-8685-9_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-8685-9_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8684-2
Online ISBN: 978-981-15-8685-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)