Abstract
Computer vision is a consistent and advanced technique for image processing with the propitious outcome an enormous potential. A computer vision has been strongly adopted in the heterogeneous domain. It is also applied to various domain of agriculture that improves the quality of automation, growth of economy, and the productivity of the nation. Fruits and vegetables quality highly affects the evaluation of quality and export market. Recently, automatic visual inspection becomes very important for grading of fruits applications. In this paper, multiple features with sparse representative classifier and artificial neural network-based automatic grading of apple are done. Firstly pre-processing is done using histogram equalization to smooth the image. Then, fuzzy c-means clustering is used for segmenting the defected region. Secondly, the combination of these seven features, i.e., color moment, color histogram, color correlogram, color coherence vector Zernike moment, moment invariant, and Legendre moments is used to extract the information. Finally, the grading is done using SRC and ANN classifiers and achieve accuracy with above 96%. The agriculture industry achieves the direction of research and support technically for grading of apples using multiple features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Reference
N. Valous, L. Zheng, D.-W. Sun, J. Tan, Quality evaluation of meat cuts, in Computer Vision Technology for Food Quality Evaluation, 2nd edn. (Elsevier, 2016), pp. 175–193
L.S. Magwaza, U.L. Opara, Analytical methods for determination of sugars and sweetness of horticultural products: a review. Sci. Hortic. 184, 179–192 (2015)
V. Leemans, H. Magein, M.-F. Destain, Defect segmentation on ‘golden delicious’ apples by using color machine vision. Comput. Electron. Agric. 20(2), 117–130 (1998)
G. Rennick, Y. Attikiouzel, A. Zaknich, Machine grading and blemish detection in apples. Int. Symp. Sig. Process. Appl. 567–570 (1999)
Z Xiabo, Z. Jie-Wen, L. Youxiao, M. Holmes, Inline detection of apple defects using three color cameras system. Comput. Electron. Agric. 70, 129–134 (2010)
R.L. Radojević, D.V. Petrović, Digital parameterization of apple fruit size, shape and surface spottiness. Afr. J. Agric. Res. 6(13), pp. 3131–3142 (2011)
A. Gopal, R. Subhasree, V.K. Srinivasan, Classification of color objects like fruits using probability density function. Int. Conf. Mach. Vis. Image Process. 1–4 (2012)
S.A Khoje, S.K. Bodhe, A. Adsul, Automated skin defect identification system for fruit grading based on discrete curvelet transform. Int. J. Eng. Technol. 5(4) (2013)
A. Vani, D.S. Vinod, Automatic quality evaluation of fruits using probabilistic neural network approach. Int. Conf. Contemp. Comput. Inf. 308–331 (2014)
S.R. Dubey, A.S. Jalal, Apple disease classification using color, texture and shape features from images. 10(5), 819–826 Sign. Image Video Process. (2015)
S. Khade, P. Pandhare, S. Navale, K. Patil, V. Gaikwad, Fruit quality evaluation using k-means clustering approach. Int. J. Adv. Sci. Eng. Technol. 4(2), 27–31 (2016)
M.A.H. Ali, K.W. Thai, Automatic fruit grading system, in International Symposium on Robotics and Manufacturing Automation (2017)
A. Bhargava, A. Bansal, Fruits and vegetables quality evaluation using computer vision: a review. J. King Saud Univ. Comput. Inf. Sci. (2018) https://doi.org/10.1016/j.jksuci.2018.06.002
A. Bhargava, A. Bansal, Quality evaluation of mono and bi-colored apples with computer vision and multispectral imaging. Multimedia Tools Appl. (2019). https://doi.org/10.1007/s11042-019-08564-3
A. Bhargava, A. Bansal, Automatic detection and grading of multiple fruits by machine learning. Food Analyt. Methods (2019). https://doi.org/10.1007/s12161-019-01690-6
J. Blasco, N. Aleixos, E. Molto, Machine vision system for automatic quality grading of fruit. Biosyst. Eng. 85(4), 415–42 (2003)
S.R. Kalluri, Apple, Orange, Banana Images are retrieved 15 Jan 2018 from https://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification
D. Unay, B. Gosselin, Artificial neural network-based segmentation and apple grading by Machine vision. Int. Conf. Image Process. (2005)
Anonymous, Commission Regulation (EC) No 85/2004 of 15 January 2004 on marketing standards for apples. Off. J. Eur. Union L. 13, 3–18
V. Ashok, D.S. Vinod, Using K-means cluster and fuzzy C means for defect segmentation in fruits. Int. J. Comput. Eng. Technol. 11–19 (2014)
X. Ou, W. Pan, P. Xiao, Vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int. J. Pharm. 460(2), 28–32 (2014)
J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, M. Yi, Robust face recognition via sparse representation, vol. 31, in IEEE Transactions Pattern Analysis, and Machine Intelligence (2009), pp. 210–227
X. Wen, J. Fang, M. Diao, C. Zhang, Artificial neural network modeling of dissolved oxygen in the Heihe River. Northwestern China. Environ. Monit. Assess 185(5), 4361–4371 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhargava, A., Bansal, A. (2022). Grading of Variety of Bi and Mono-Colored ApplesT. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_35
Download citation
DOI: https://doi.org/10.1007/978-981-16-1249-7_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1248-0
Online ISBN: 978-981-16-1249-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)