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1723730 Vol 10 · Issue 4 Download Paper

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

Manasa T. N. M. P. Pushpalatha

Subject area: Science,Engineering and Technology  ·  Area of research: Image Processing

DOI: 10.64388/IREV10I4-1723730

Abstract

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.

Keywords

banana ripeness classification, Nendra Bale, Random Forest, SVM, k-Nearest Neighbours

References

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How to cite this paper

Manasa T. N., M. P. Pushpalatha "Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest" Iconic Research And Engineering Journals Volume 10 Issue 4 2026 Page 648-655 https://doi.org/10.64388/IREV10I4-1723730
Manasa T. N., M. P. Pushpalatha "Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest" Iconic Research And Engineering Journals, vol. 10, no. 4, Oct. 2026, doi: https://doi.org/10.64388/IREV10I4-1723730
Manasa T. N., M. P. Pushpalatha (2026). Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest. Iconic Research And Engineering Journals, 10(4). doi: https://doi.org/10.64388/IREV10I4-1723730
Manasa T. N., M. P. Pushpalatha "Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest" Iconic Research And Engineering Journals, vol. 10, no. 4, Oct. 2026. Crossref, https://doi.org/10.64388/IREV10I4-1723730
@article{1723730,
      author = {Manasa T. N., M. P. Pushpalatha},
      title = {Machine Learning for Ripeness Classification of Nendra Bale Banana: A Classical Approach Using SVM, kNN, and Random Forest},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {648-655},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723730.pdf},
      abstract = {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.},
      keywords = {banana ripeness classification, Nendra Bale, Random Forest, SVM, k-Nearest Neighbours},
      month = {October},
      doi = {https://doi.org/10.64388/IREV10I4-1723730}
  }