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http://dx.doi.org/10.9717/kmms.2022.25.10.1386

Automatic Fruit Grading Using Stacking Ensemble Model Based on Visual and Physical Features  

Kim, Min-Ki (School of Computer Science, Gyeongsang National University, Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.25, no.10, 2022 , pp. 1386-1394 More about this Journal
Abstract
As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.
Keywords
Fruit Grading; Stacking Ensemble Model; CNN; Perceptron;
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1 M. Khojastehnazhand, M. Omid, and A. Tabatabaeefar, "Development of a Lemon Sorting System Based on Color and Size," African Journal of Plant Science, Vol. 4, No. 4, pp. 122-127, 2010.
2 P. Moallem, A. Serajoddin, and H. Pourghassem, "Computer Vision-Based Apple Grading for Golden Delicious Apples based on Surface Features," Information Processing in Agriculture, Vol. 4, No. 1, pp. 33-40, 2017.   DOI
3 N.M. Trieu and N.T. Thinh, "Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network," Applied Sciences, Vol. 11, No. 22, pp. 1-13, 2021.
4 G. Capizzi, G.L. Sciuto, C. Napoli, and E. Tramontana, "A Novel Neural NetworksBased Texture Image Processing Algorithm for Orange Detects Classification," International Journal of Computer Science and Applications, Vol. 13, No. 2, pp. 45-60, 2016.
5 M. Kim, "Tomato Crop Disease Classification Using an Ensemble Approach Based on a Deep Neural Network," Journal of Korea Multimedia Society, Vol. 23, No. 10, pp. 1250-1257, 2020   DOI
6 S. Naik and B. Patel, "Machine Vision Based on Fruit Classification and Grading - A Review," International Journal of Computer Applications, Vol. 170, No. 9, pp. 22-34, 2017.
7 T.K. AmeethaJunaina, B.E. Abishek, V. Rajendren, S. Mohammed, and P.S. Kumar, "A Survey on Fresh Produce Grading Algorithms Using Machine Learning and Image Processing Techniques," IOP Conference Series: Materials Science and Engineering, Vol. 981, No. 4, 042084, pp. 1-7, 2020.
8 H. Dang, J. Song, and Q. Guo, "A Fruit Size Detecting and Grading System Based on Image Processing," Proceeding of the International Conference on Intelligent HumanMachine Systems and Cybernetics, pp. 83-86, 2010.
9 N.B.A. Mustafa, S.K. Ahmed, Z. Ali, W.B. Yit, A.A.Z. Abidin, and Z.A.M. Sharrif, "Agricultural Produce Sorting and Grading using Support Vector Machines and Fuzzy Logic," Proceedings of the IEEE International Conference on Signal and Image Processing Applications, pp. 391-396, 2009.
10 L. Fu, S. Sun, R. Li, and S. Wang, "Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera," Sensors, Vol. 16, No. 7, 1012, pp. 1-14, 2016.   DOI
11 AI Hub, Agricultural Products QC dataset, https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=149 (accessed October 1, 2022).
12 Y. Yu, S.A. Velastin, and F. Yin, "Automatic Grading of Apples based on Multi-features and Weighted K-means Clustering Algorithm," Information Processing in Agriculture, Vol. 7, No. 4, pp. 556-565, 2020.   DOI
13 N. Ismail and O.A. Malik, "Real-Time Visual Inspection System for Grading Fruits Using Computer Vision and Deep Learning Techniques," Information Processing in Agriculture, Vol. 9, pp. 24-37, 2022.   DOI
14 Y. Li, X. Feng, Y. Liu, and X. Han, "Apple Quality Identification and Classification by Image Processing Based on Convolutional Neural Networks," Scientific Reports, 2021.