International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF
Volume 73 | Issue 11 | Year 2025 | Article Id. IJCTT-V73I11P105 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I11P105

Optimized Radial Basis Function Neural Network for Automated Classification of Apple Quality

Aditya J Parida, Manas Ranjan Senapati, Soumya Das

Received Revised Accepted Published
19 Sep 2025 27 Oct 2025 14 Nov 2025 29 Nov 2025

Citation :

Aditya J Parida, Manas Ranjan Senapati, Soumya Das, "Optimized Radial Basis Function Neural Network for Automated Classification of Apple Quality," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 11, pp. 30-36, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I11P105

Abstract

Apples are still very much a global health food, which also plays a large role in agriculture. We must see to it that we have the same high standards in sorting and distribution, which in turn will do much to reduce post-harvest waste. This study reports on a developed “Radial Basis Function Neural Network (RBFNN)” model for the automatic classification of apples into good and defective. We have put together a system that uses a set of numerical features from our data, which includes size, weight, sugar content, crispness, juice content, ripeness, and acid level, which also serves to eliminate the need for extra feature extraction or image processing. We also report that we have improved upon radial center and spread parameters, which in turn have improved the accuracy and learning convergence. The model achieves notable performance on 1200 test samples, recording an accuracy of 89.83%, precision of 94.14%, recall of 88.33%, specificity of 92.06%, and an F1-score of 91.12%. The findings demonstrate that the optimized RBFNN effectively balances computational efficiency with predictive reliability, making it a suitable solution for real-time quality inspection in automated fruit sorting and supply chain systems.

Keywords

Acidity, Apple quality classification, Classification accuracy, Radial basis function neural network, Real-time assessment.

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