Volume 9 • Issue 1 • PP: 37–52 • 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).
View PDF Abstract
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.
Keywords
References
[1] Aoun, M.: Unlocking heirloom diversity: A pathway to bridging global challenges in modern apple cultivation. Frontiers in Horticulture 2, 1268970 2023.
[2] Xu, B., Cui, X., Ji, W., Yuan, H., Wang, J.: Apple Grad-ing Method Design and Implementation for Automatic Grader Based on Improved YOLOv5. Agriculture 13(1), 124 2023.
[3] Hassan, N.M.H., Elshoky, B.R.G., Mabrouk, A.M.M.: Quality of performance evaluation of ten machine learn-ing algorithms in classifying thirteen types of apple fruits. Indonesian Journal of Electrical Engineering and Computer Science 30(1), 102–109 2023.
[4] Knott, M., Perez-Cruz, F., Defraeye, T.: Facilitated ma-chine learning for image-based fruit quality assessment. Journal of Food Engineering 345, 111401 2023.
[5] Lee, D., Oh, J., Choi, Y., Lee, D., Lee, H., Shim, S.-B., Ha, Y.: Development of YOLO-based apple quality sorter. Korean Journal of Agricultural Science 50(3), 415–424 2023.
[6] Liu, X., Le Bourvellec, C., Yu, J., Zhao, L., Wang, K., Tao, Y., Renard, C.M.G.C., Hu, Z.: Trends and challenges on fruit and vegetable processing: Insights into sustainable, traceable, precise, healthy, intelligent, personalized and local innovative food products. Trends in Food Science & Technology 125, 12–25 2022.
[7] Ellahi, R.M., Wood, L.C., Bekhit, A.E.-D.A.: Blockchain-based frameworks for food traceability: A systematic review. Foods 12(16), 3026 2023.
[8] Hassoun, A., Jagtap, S., Garcia-Garcia, G., Trollman, H., Pateiro, M., Lorenzo, J.M., Trif, M., Rusu, A.V., Aadil, R.M., Šimat, V., Cropotova, J., Câmara, J.S.: Food quality 4.0: From traditional approaches to digital-ized automated analysis. Journal of Food Engineering 337, 111216 2023.
[9] Ferrer-Ferrer, M., Ruiz-Hidalgo, J., Gregorio, E., Vila-plana, V., Morros, J.-R., Gené-Mola, J.: Simultaneous fruit detection and size estimation using multitask deep neural networks. Biosystems Engineering 233, 63–75 2023.
[10] Rosati, R., Romeo, L., Cecchini, G., Tonetto, F., Viti, P., Mancini, A., Frontoni, E.: From knowledge-based to big data analytic model: A novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0. Journal of Intelligent Man-ufacturing 34(1), 107–121 2023.
[11] Yu, F., Lu, T., Xue, C.: Deep learning-based intelli-gent apple variety classification system and model inter-pretability analysis. Foods 12(4), 885 2023.
[12] Albahra, S., Gorbett, T., Robertson, S., D’Aleo, G., Ku-mar, S.V.S., Ockunzzi, S., Lallo, D., Hu, B., Rashidi, H. H.: Artificial intelligence and machine learning overview in pathology & laboratory medicine: A gen-eral review of data preprocessing and basic supervised concepts. Seminars in Diagnostic Pathology 40(2), 71–87 2023.
[13] Ghazal, S., Qureshi, W.S., Khan, U.S., Iqbal, J., Rashid, N., Tiwana, M.I.: Analysis of visual features and clas-sifiers for fruit classification problem. Computers and Electronics in Agriculture 187, 106267 2021.
[14] Ahmad, T., Madonski, R., Zhang, D., Huang, C., Mu-jeeb, A.: Data-driven probabilistic machine learning in sustainable smart energy systems: Key developments, challenges, and future research opportunities in the con-text of smart grid paradigm. Renewable and Sustainable Energy Reviews 160, 112128 2022.
[15] Rajwar, K., Deep, K., Das, S.: An exhaustive review of the metaheuristic algorithms for search and optimiza-tion: Taxonomy, applications, and open challenges. Ar-tificial Intelligence Review 56(11), 13187–13257 2023.
[16] El-kenawy, E.-S.M., Khodadadi, N., Mirjalili, S., Abdel-hamid, A.A., Eid, M.M., Ibrahim, A.: Greylag Goose Optimization: Nature-inspired optimization algorithm. Expert Systems with Applications 238, 122147 2024.
[17] Bhargava, A., Bansal, A.: Classification and grading of multiple varieties of apple fruit. Food Analytical Methods 14(7), 1359–1368 2021.
[18] Biffi, L.J., Mitishita, E., Liesenberg, V., Santos, A.A., Gonçalves, D.N., Estrabis, N.V., Silva, J.d.A., Osco, L. P., Ramos, A.P.M., Centeno, J.A.S., Schimalski, M.B., Rufato, L., Neto, S.L.R., Marcato Junior, J., Gonçalves, W.N.: ATSS deep learning-based approach to detect apple fruits. Remote Sensing 13(1), 1 2021.
[19] Çetin, N., Karaman, K., Kavuncuo˘glu, E., Yıldırım, B., Jahanbakhshi, A.: Using hyperspectral imaging tech-nology and machine learning algorithms for assessing internal quality parameters of apple fruits. Chemomet-rics and Intelligent Laboratory Systems 230, 104650 2022.
[20] Chao, X., Sun, G., Zhao, H., Li, M., He, D.: Identifica-tion of apple tree leaf diseases based on deep learning models. Symmetry 12(7), 7 2020.
[21] Chen, J., Han, J., Liu, C., Wang, Y., Shen, H., Li, L.: A deep-learning method for the classification of apple varieties via leaf images from different growth periods in natural environment. Symmetry 14(8), 8 2022.
[22] Chu, P., Li, Z., Lammers, K., Lu, R., Liu, X.: Deep learning-based apple detection using a suppression mask R-CNN. Pattern Recognition Letters 147, 206–211 2021.
[23] Gao, F., Fang, W., Sun, X., Wu, Z., Zhao, G., Li, G., Li, R., Fu, L., Zhang, Q.: A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard. Computers and Electronics in Agriculture 197, 107000 2022.
[24] Khan, A.I., Quadri, S.M.K., Banday, S., Latief Shah, J.: Deep diagnosis: A real-time apple leaf disease de-tection system based on deep learning. Computers and Electronics in Agriculture 198, 107093 2022.
[25] Li, Y., Feng, X., Liu, Y., Han, X.: Apple quality identifi-cation and classification by image processing based on convolutional neural networks. Scientific Reports 11(1), 16618 2021.
[26] Majeed, Y., Zhang, J., Zhang, X., Fu, L., Karkee, M., Zhang, Q., Whiting, M.D.: Deep learning-based seg-mentation for automated training of apple trees on trellis wires. Computers and Electronics in Agriculture 170, 105277 2020.
[27] Mirbod, O., Choi, D., Heinemann, P.H., Marini, R.P., He, L.: On-tree apple fruit size estimation using stereo vision with deep learning-based occlusion handling. Biosystems Engineering 226, 27–42 2023.
[28] Miranda, J.C., Arnó, J., Gené-Mola, J., Lordan, J., Asín, L., Gregorio, E.: Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples. Computers and Electronics in Agricul-ture 214, 108302 2023.
[29] Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S., Auat Cheein, F.: Apple orchard production estima-tion using deep learning strategies: A comparison of tracking-by-detection algorithms. Computers and Elec-tronics in Agriculture 204, 107513 2023.
[30] Wang, X.A., Tang, J., Whitty, M.: DeepPhenology: Es-timation of apple flower phenology distributions based on deep learning. Computers and Electronics in Agri-culture 185, 106123 2021.
[31] Wu, J., Yang, G., Yang, H., Zhu, Y., Li, Z., Lei, L., Zhao, C.: Extracting apple tree crown information from remote imagery using deep learning. Computers and Electronics in Agriculture 174, 105504 2020.
[32] Xuan, G., Gao, C., Shao, Y., Zhang, M., Wang, Y., Zhong, J., Li, Q., Peng, H.: Apple detection in natu-ral environment using deep learning algorithms. IEEE Access 8, 216772–216780 2020.
[33] Guo, Z., Zhang, Y., Wang, J., Liu, Y., Jayan, H., El-Seedi, H.R., Alzamora, S.M., Gómez, P.L., Zou, X.: Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning. Comput-ers and Electronics in Agriculture 212, 108127 2023.
[34] Zou, Z., Long, T., Wang, Q., Wang, L., Chen, J., Zou, B., Xu, L.: Implementation of apple’s automatic sorting system based on machine learning. Food Science and Technology 42, 24922 2022.
[35] Zhang, X., He, L., Zhang, J., Whiting, M.D., Karkee, M., Zhang, Q.: Determination of key canopy param-eters for mass mechanical apple harvesting using su-pervised machine learning and principal component analysis (PCA). Biosystems Engineering 193, 247–263 2020.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.