Journal of Food Science and Technology

, Volume 56, Issue 4, pp 2305–2311 | Cite as

Development of a computer vision system to estimate the colour indices of Kinnow mandarins

  • Lingaraj Hadimani
  • Neerja MittalEmail author
Short Communication


Maturity of a citrus fruit is generally expressed by a numerical value called citrus colour index (CCI). The success of methods employed in estimating the maturity depends on the cultivar and climatic conditions of growing regions. In this work, an image processing based method using CIELAB color model has been developed to estimate the CCI of Kinnow mandarin fruits. A polynomial transformation based camera characterization method was employed to reduce the number of transformations required for RGB to \( L^{*} a^{*} b^{*} \) colour space transformation, which resulted into a colour difference of 2.191 with CIELAB \( \Delta E^{*} \) 2000 colour difference formula. In order to analyse the performance of this method, linear regression and partial least square (PLS) models were built on a dataset of 271 Kinnow fruit images wherein spectrophotometer was used for the validation of computed CCI values. The proposed method achieved a high adjusted \( R^{2} \) value of 0.9660 using PLS regression, which ascertain the feasibility of image processing based system in estimating the maturity of Kinnow fruits. Additionally, a correlation analysis between colour coordinates and physicochemical properties was conducted to analyze the relation between the fruit’s external peel colour and its internal characteristics.


Computer vision system Citrus colour index Colour characterization Colour difference Physicochemical parameters Kinnow mandarins 



The authors would like to express their gratitude to Dr. H. K. Sardana, chief scientist, CSIR-CSIO, Chandigarh for his valuable guidance and suggestions. The authors would like to express their sincere thanks to Mr. Amit Gilla for providing the fruit samples at regular intervals of time. Special thanks to Mr. Sandeep Singhai, Mr. Supankar Das and Mr. Sunil Kumar for their help in designing and setting up the image acquisition chamber.

Supplementary material

13197_2019_3641_MOESM1_ESM.pdf (318 kb)
Supplementary material 1 (PDF 317 kb)


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Copyright information

© Association of Food Scientists & Technologists (India) 2019

Authors and Affiliations

  1. 1.Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
  2. 2.Department of Computational InstrumentationCSIR-Central Scientific Instruments OrganisationChandigarhIndia
  3. 3.KLE Dr. M.S.Sheshgiri College of Engineering and TechnologyBelagaviIndia

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