Volume 9 • Issue 1 • PP: 53-71 • 2025
Comparative Analysis of Machine Learning Models and Greylag Goose Optimization for High-Accuracy Apple Quality Grading
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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A significant barrier to sustaining and achieving successful quality evaluation of apples has been the biases and inefficiencies of manual inspection, which cannot be effectively applied to large-scale industrial processing. This paper fills this gap by building an automated machine learning system for apple quality classification, which combines physical (size, weight, firmness) and sensory (sweetness, acidity, juiciness) features. Four baseline models, including K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Gradient Boosting, were trained on a preprocessed dataset. Imputation, categorical encoding, and feature scaling were used. To enhance predictive accuracy, hyperparameter tuning of three metaheuristic optimization algorithms — Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Greylag Goose Optimization (GGO) — was employed. The findings indicated that KNN had the highest baseline accuracy of 93.6% and that its accuracy improved to 98.6% with GGO optimization, which was better than that of GA and PSO. These results indicate that GGO-optimized KNN can provide a high-quality and scalable approach to assessing the quality of apples in the industry, offering enhanced accuracy, reduced variability, and a high degree of practical applicability in supply chain decision making, thereby increasing customer satisfaction and decreasing losses after harvesting.
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References
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