Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Jayanta Kumar Basak; Bolappa Gamage Kaushalya Madhavi; Bhola Paudel; Na Eun Kim; Hyeon Tae Kim

doi:10.3390/foods11142086

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Foods. 2022 Jul 13;11(14):2086. doi: 10.3390/foods11142086.

Authors

Jayanta Kumar Basak^{1

2}, Bolappa Gamage Kaushalya Madhavi³, Bhola Paudel³, Na Eun Kim³, Hyeon Tae Kim³

Affiliations

¹ Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Korea.
² Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
³ Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Korea.

Abstract

Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.

Keywords: colour spaces; image processing technique; multiple linear regression; pH; strawberry; support vector machine regression; total soluble solids.

Abstract

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