Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest

acceptance, and overall shelf life. Nendra Bale (Nendran), a dual-purpose, starch-rich Indian banana cultivar widely used for both table consumption and cooking, exhibits a ripening pattern that is markedly more texture-driven than colour-driven, distinguishing it from commercially dominant, colour-dominant cultivars. This study introduces an image-based framework for classifying the ripening stage of Nendra Bale bananas using classical machine learning. A first-hand dataset of 80 raw images across four ripening stages (Green, Mid-ripe, All-Yellow, Overripe) was collected under everyday mobile-camera conditions and augmented to 5,391 images to support more robust model training. Three classical classifiers (Support Vector Machine, k-Nearest Neighbours, Random Forest) were evaluated on Histogram of Oriented Gradients (HOG) features under both raw and augmented conditions. On the raw dataset, kNN gave the highest accuracy (76.47%), ahead of SVM and Random Forest (70.59% each); once the dataset was augmented, SVM became the strongest and most consistent model (81.60%), ahead of kNN (78.62%) and Random Forest (74.53%). A probabilistic stage-to-shelf-life mapping is proposed to convert predictions into an expected remaining life in days. The study offers a low-cost, non-destructive, deployable approach for quality assessment and post-harvest decision support for a texture-dominant, dual-purpose Indian banana cultivar.