Colour, Texture and Geometric Feature Based Classification of Banana Varieties Using Interpretable Machine Learning

Non-destructive image-based classification of fruit varieties is essential for optimizing cost, nutritional benefits and supply chain management in food system. In this study we propose an interpretable machine learning framework for the multi-class classification of banana varieties using handcrafted image features extracted from colour, texture and geometric properties. A total of 195 banana images from five varieties were collected and expanded the dataset to 669 samples through rotational augmentation to enhance model generalization. Five supervised learning models, viz., k-Nearest Neighbours, Naïve Bayes, Random Forest, Decision Tree, and Support Vector Machine, were implemented using standard evaluation metrics. Among all models, Random Forest demonstrated the highest performance with an accuracy of 98.5 per cent and an MCC of 0.9814. Statistical validation was performed using one-way ANOVA and post-hoc Tukey HSD tests, which confirmed significant difference between model performances. Additionally, SHAP analysis provided insights into feature importance and model decision processes. The findings suggest ensemble learning models, especially Random Forest offer a compelling combination of accuracy and interpretability for agricultural classification tasks. The proposed approach enables applications in automated fruit sorting and mobile based advisory platform in smart agriculture.