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.

草莓成熟度的准确分类是确保高质量食品、优化采收和储存过程以及促进消费者健康的重要方面。尽管为此目的开发了几种基于非破坏性计算机视觉的系统，但在草莓成熟阶段分类过程中，不同颜色空间对机器学习模型性能的影响仍未得到充分研究。在此背景下，将三种机器学习模型，即高斯朴素贝叶斯 (GNB)、支持向量机 (SVM) 和前馈人工神经网络 (FANN) 与四种颜色空间（RGB、HLS、CIELab 和 YCbCr）相结合和生物识别特征，以评估色彩空间对草莓成熟度分类机器学习模型性能的有效性。以此目的，收集了 1210 个样品，并手动将其分为四个成熟阶段。通过组合每个颜色空间值、生物特征和相应的成熟阶段来创建数据集，并将其用作模型的输入。结果表明，CIELab 颜色空间下的 FANN 准确率最高，为 96.7%，其次是 GNB 和 SVM，两者在 CIELab 颜色空间下的准确度相同，均为 95.46%。使用 GNB 分类器在 RGB 颜色空间中观察到的最低准确度为 92.15%。在这项研究中，未成熟和过熟阶段的分类更加准确，而中间成熟阶段对模型来说更具挑战性。此外，观察到模型的准确性受到所选颜色空间和分类模型的影响。此外，