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

Abstract

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 Δ 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.

Code and data availability

The paper describes a computer vision system for estimating citrus colour index of Kinnow mandarins using 271 fruit images, but no public dataset, image repository, analysis code, or trained model is deposited. The only supplementary material (Online Resource 1, PDF) is stated to be 'available to authorized users' and,

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