Abstract
This research reports the comparison study of manual (digital vernier caliper, DVC) method and application of computer vision system (CVS) for evaluation of size attributes (length, width and thickness) of two mango cultivars (Chausa and Dashehari). The color RGB (red, green and blue) camera was used in CVS for capturing of required profile image of fruit. The profile image was then processed and developed algorithms for image analysis and extraction of fruit’s quality attributes. Various morphological features such as area, perimeter, Max Feret diameter (MFD), hydraulic radius, Waddel disk diameter (WDD), elongation factor, compactness factor, Heywood circulatory factor and type factor of both cultivars were also evaluated using developed algorithms and attempted to describe the morphology of the outline and shape of the biological object. Area, perimeter, Max Feret diameter and Waddel disk diameter, further, correlated with the weight and volume of fruit. Various multilinear regression (MLR) models were developed for determining weight and volume of mango fruit of both cultivars using dimensional characteristics (length, width and thickness) of mango fruit evaluated by CVS. The major diameter (single variable) and all three (major, intermediate and minor) diameter (multiple variables)-based models were identified as the best models. Similarly, CVS has yielded above 97.9%, 93.5% and 92.5% accuracy in estimating mango fruits major, intermediate and minor diameters of both cultivars.
Similar content being viewed by others
References
Anonymous (2010) Newsletters, http://www.icar.org.in/. Accessed 10 May 2011s
Anonymous (2012) O & M Newsletter. Maine Department of Environmental Protection, Bureau of Land and Water Quality. www.maine.gov/dep/water/newsletter/omnews/february2012. Accessed 12 September 2012
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310
Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160
Bland JM, Altman DG (2007) Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 17:571–582
Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision—a review. J Food Eng 61:3–16
Dorvlo SY, Bani RJ, Sinayobe E (2012) Prediction of volume, weight and surface area of banana (Musa acuminata) using picture image analysis. J Food Agric Environ 10(3&4):112–114
Jahns G, Nielsen HM, Paul W (2001) Measuring image analysis attributes and modeling fuzzy consumer aspects for tomato quality grading. Comput Electron Agric 31:17–29
Khanali M, Ghasemi-Varnamkhasti MG, Tabatabaeefar A, Mobli H (2007) Mass and volume modelling of tangerine (Citrus reticulate) fruit with some physical attributes. Int Agrophys 21:329–334
Khezri SL, Rashidi M, Gholami M (2012) Modeling of peach mass based on geometrical attributes using linear regression models. Am Eur J Agric Environ Sci 12(7):991–995
Khoshnam F, Tabatabaeefar A, Ghasemi-Varnamkhasti M, Borghei AM (2007) Mass modeling of pomegranate (Punica granatum L) fruit with some physical characteristics. Sci Hortic 114:21–26
Lorestani AN, Kazemi A (2012) Mass Modeling of castor seed (Ricinus Communis) with some geometrical attributes. Int J Agric For 2(5):235–238
Lorestani AN, Tabatabaeefar A (2006) Modeling the mass of kiwifruit by geometrical attributes. Int Agrophys 20:135–139
Maduako JN, Faborode MO (1990) Some physical properties of cocoa pods in relation to primary processing. IFE J Technol 2:1–7
Miller WM, Peleg K, Briggs P (1988) Automated density separation for freeze damaged citrus. Appl Eng Agric 4(4):344–348
Mohsenin NN (1978) Physical properties of plant and animal materials. Gorden and Breach Science Publisher, New York
Mohsenin NN (1986) Physical properties of plant and animal materials: structure, physical characteristics and mechanical properties. Gordon and Breach Press Publishers, New York
Naderi-Boldaji M, Khadivi-khub A, Tabatabaeefar A, Ghasemi-Varnamkhasti M, Zamani Z (2008) Some physical properties of sweet cherry (Prunus avium L.) Fruit. Am Eur J Agric Environ Sci 3(4):513–520
NIC (2000) Particle measurements, ch. 10. IMAQ Vision Concepts Manual, p10-11October 2000 Edition Part Number 322916A-01
NIC: National Instrument Corporation (1997) IMAQ™ Vision for G Reference Manual. Chapter 8-quantitave analysis, Part Number 321379B-01, pp 1–18
Patel KK, Kar A, Jha SN, Khan MA (2012) Machine vision system: a tool for on-line sorting and grading of agricultural foods and produces. J Food Sci Technol 49(2):123–141
Patel KK, Kar A, Khan MA (2012) Nondestructive food quality evaluation techniques: principle and potential. Agric Eng Today 36(4):29–34
Patel KK, Kar A, Khan MA (2015) Recent developments in applications of MRI techniques for foods and agricultural produce—an overview. J Food Sci Technol 52(1):1–26
Patel KK, Kar A, Khan MA (2019) Potential of reflected UV imaging technique for detection of defects on the surface area of mango. J Food Sci Technol 56(3):1295–1301
Seyedabadi E, Khojastehpour M, Sadrnia H, Saiedirad M-H (2011) Mass modeling of cantaloupe based on geometric attributes: a case study for Tile Magasi and Tile Shahri. Sci Hortic 130:54–59
Spreer W, Muller J (2011) Estimating the mass of mango fruit (Mangifera indica, cv.ChokAnan) from its geometric dimensions by optical measurement. Comput Electron Agric 30:30
Tabatabaeefar A, Rajabipour A (2005) Modeling the mass of apples by geometrical attributes. Sci Hortic 105:373–382
Wardowski WF, Miller WM, Grierson W (1997) Separation and grading of freeze damaged citrus fruits. Circular 372, Institute of Food and Agricultural Sciences, University of Florida
Acknowledgements
This work was supported by the National Agricultural Innovation Project, Indian Council of Agricultural Research through its subproject entitled “Development of non-destructive system for evaluation of microbial and physicochemical quality parameters of mango” (C1030).
Funding
Financial support under the World Bank funded National Agricultural Innovation Sub-Project on “Development of nondestructive system for evaluation of microbial and physic-chemical quality parameters of mango” being implemented at the Indian Agricultural Research Institute is duly acknowledged (C1030).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All the authors declare that they have no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patel, K.K., Kar, A. & Khan, M.A. Development and an Application of Computer Vision System for Nondestructive Physical Characterization of Mangoes. Agric Res 9, 109–124 (2020). https://doi.org/10.1007/s40003-019-00400-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40003-019-00400-2