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@article{202083,
        author = {D. R. Solanke and A.B Manwar and K. D. Chinchkhede},
        title = {Implementation and Analysis of Date Fruit Grading Framework using Logistic Regression Classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6122-6129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202083},
        abstract = {Date fruit grading plays a crucial role in ensuring quality assurance, market value optimization, and automated post-harvest processing in the agri-food industry. This paper presents the implementation and analysis of a Date Fruit Grading Framework using a Logistic Regression classifier, focusing on multi-class classification of seven commercially significant date varieties, namely Berhi, Deglet, Dokol, Iraqi, Rotana, Safavi, and Sogay. The proposed framework utilizes discriminative feature representations extracted from the dataset and evaluates the effectiveness of Logistic Regression against other conventional machine learning classifiers. Experimental results demonstrate that the Logistic Regression model achieves an overall classification accuracy of 91.11% with a mean squared error (MSE) of 1.10, outperforming Support Vector Machine, XGBoost, KNN, and Random Forest, Decision Tree, and AdaBoost classifiers. Detailed performance analysis reveals strong class-wise precision, recall, and F1-score values, with a weighted average precision, recall, and F1-score of 0.91, indicating consistent predictive capability across all date varieties. The confusion matrix analysis further confirms robust inter-class separability, particularly for Dokol, Safavi, and Rotana classes, which exhibit minimal misclassification. These results validate the suitability of Logistic Regression as a reliable and computationally efficient baseline classifier for automated date fruit grading applications. The proposed framework can be effectively deployed in real-time quality inspection systems to support intelligent agricultural decision-making and scalable grading solutions.},
        keywords = {Automated grading, Date fruit classification, Logistic regression, Machine learning, multi-class classification, Quality assessment.},
        month = {May},
        }