Abstract
Accurate classification of strawberry ripeness is a crucial aspect of ensuring high-quality food products, optimizing harvesting and storage processes, and promoting consumer health. Although several non-destructive computer vision-based systems have been developed for this purpose, the influence of different colour spaces on machine-learning model performance during the ripeness stage classification of strawberries remains underexplored. In this context, three machine-learning models, namely Gaussian Naïve Bayes (GNB), support vector machine (SVM) and feed-forward artificial neural networks (FANN), were combined with four colour spaces (RGB, HLS, CIELab and YCbCr) and biometrical characteristics to evaluate the effectiveness of colour spaces on the performance of machine-learning models for classifying strawberry ripeness. For this purpose, 1210 samples were collected and manually classified into four ripeness stages. A dataset was created by combining each colour space value, biometrical properties, and corresponding ripeness stage, which was used as inputs to the models. The results indicated that FANN with CIELab colour space achieved the highest accuracy of 96.7%, followed by GNB and SVM, both having equal accuracy of 95.46% in CIELab colour space. The least accuracy of 92.15% was observed in RGB colour space with the GNB classifier. In this study, the unripe and over-ripe stages were more accurately classified, while intermediate ripening stages proved to be more challenging for the models. Furthermore, the accuracy of models was observed to be influenced by both the colour space and classification model selected. Additionally, further research is needed to investigate other features that could improve the performance of models for strawberry ripeness classification.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Acknowledgements
The authors would like to thank the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (717001-7) for financial support to conduct the research.
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This research has been financially supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (717001-7).
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Conceptualization, methodology, software analysis, formal analysis, writing—original draft preparation, and visualization by S.K.; validation by J.K.B., B.P. and H.T.K.; resources by N.C.D., N.-E.K. and M.Y.K.; writing—review and editing by S.K., J.K.B. and B.P.; Project administration, and funding acquisition by H.T.K. All authors have read and agreed to the published version of the manuscript.
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Karki, S., Basak, J.K., Paudel, B. et al. Classification of strawberry ripeness stages using machine learning algorithms and colour spaces. Hortic. Environ. Biotechnol. 65, 337–354 (2024). https://doi.org/10.1007/s13580-023-00559-2
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DOI: https://doi.org/10.1007/s13580-023-00559-2