Prototype of AI-based Grading of Mahachanok Mangoes for Export Using Deep Learning Techniques
DOI:
https://doi.org/10.57260/stc.2026.1260Keywords:
Artificial intelligence, Mahachanok mango grading, Deep learning techniques, CiRA CORE programAbstract
This research aims to develop a prototype of using artificial intelligence (AI) in grading Mahachanok mangoes for export by applying deep learning techniques to increase efficiency and accuracy in the grading process of Mahachanok mangoes. Data was collected from 1,000 Mahachanok mango photos, which were graded under the following conditions: peel defects (the main factor), maturity (85-90%), shape (no distortion), and weight (between 300-500 grams), which were graded as A, B, C, and D. The CiRA CORE artificial intelligence tool was used to detect and classify Mahachanok mangoes, which were trained to recognize the characteristics of each grade to serve as a prototype for export grades. The test results show that the AI prototype has a high overall performance, with an average F1-Score of 91.90 percent and outstanding performance in classifying Mahachanok mangoes, with Grade A having the highest F1-Score at 96.20% (Precision 97.40%, Recall 95.00%), followed by Grade B at 95.20% (Precision 94.00%, Recall 96.50%), while Grade C and D have F1-Score at 89.00% (Precision 90.00%, Recall 88.00%) and 87.20% (Precision 85.50%, Recall 89.00%), respectively.
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กรมส่งเสริมการค้าระหว่างประเทศ. (2567). รายงานสถานการณ์สินค้าเกษตรและผลไม้ไทยในตลาดโลก. กระทรวงพาณิชย์.
Capizzi, G., Sciuto, G., Napoli, C., Tramontana, E., & Wozniak, M. (2016). A Novel Neural Networks-Based Texture Image Processing Algorithm for Orange Defects Classification. International Journal of Computer Science and Applications (IJCSA), 13( 2), 45-60. https://iris.uniroma1.it/handle/11573/1328557
CiRA CORE Team. (2023). CiRA CORE: The Industrial AI Platform for Everyone. https://www.ciracore.com
Dubey, S. R., & Jalal, A. S. (2016). Apple disease classification using color, texture and shape features from images. SIViP 10, 819–826. https://doi.org/10.1007/s11760-015-0821-1
Kaur, H., Sawhney, B. K., & Jawandha, S. K. (2018). Evaluation of plum fruit maturity by image processing techniques. J Food Sci Technol, 55, 3008–3015. https://doi.org/10.1007/s13197-018-3220-0
Mohammed A. H., & Thai, K. W. (2017). Automated fruit grading system. IEEE 3rd International Symposium in Robotics and Manufacturing Automation (ROMA), Kuala Lumpur, Malaysia, pp. 1-6. https://doi.org/10.1109/ROMA.2017.8231734
Mohammadi, V., Kheiralipour, K., & Ghasemi-Varnamkhasti, M. (2015). Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae. 184(5), 123-128. https://www.researchgate.net/publication/271645830
Nandi, C. S., Tudu, B., & Koley, C. (2014). Machine vision based automatic fruit grading system using fuzzy algorithm. Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), Calcutta, India, pp. 26-30. https://doi.org/10.1109/CIEC.2014.6959043.
Naik, S. (2019). Non-Destructive Mango (Mangifera Indica L., CV. Kesar) Grading Using Convolutional Neural Network and Support Vector Machine. Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, pp. 26-28, http://dx.doi.org/10.2139/ssrn.3354473
Olaniyi, E. O., Oyedotun, O. K., & Adnan, K. (2017), Intelligent Grading System for Banana Fruit Using Neural Network Arbitration. Journal of Food Process Engineering, 40(1), e12335. https://doi.org/10.1111/jfpe.12335
Raut, K., & Bora, V. (2016). Assessment of Fruit Maturity using Digital Image Processing. International Journal of Science Technology and Engineering, 3(1), 273-279. http://www.ijste.org/Article.php?manuscript=IJSTEV3I1122
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deep-fruits: a fruit detection system using deep neural networks. Sensors, 16(8), 1222. https://doi.org/10.3390/s16081222
Sahu, D., & Potdar, R.M. (2017) Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. American Journal of Artificial Intelligence, 1(1), 5-14. https://www.sciencepublishinggroup.com/article/10.11648/j.ajai.20170101.12
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