Quality Assessment and Prediction of Philippine Mangoes: A Convolutional Neural Network Approach

Carlos Matthew P. Cases (1), Annamitz A. Rapliza (2), Francisco Emmanuel T. Munsayac Jr. III (3), Nilo T. Bugtai (4), Robert Kerwin D. Billiones (5), Renann G. Baldovino (6)
(1) Gokongwei College of Engineering, De La Salle University, Manila
(2) Gokongwei College of Engineering, De La Salle University, Manila
(3) Gokongwei College of Engineering, De La Salle University, Manila
(4) Gokongwei College of Engineering, De La Salle University, Manila
(5) Gokongwei College of Engineering, De La Salle University, Manila
(6) Gokongwei College of Engineering, De La Salle University, Manila
Fulltext View | Download
How to cite (IJASEIT) :
Cases, Carlos Matthew P., et al. “Quality Assessment and Prediction of Philippine Mangoes: A Convolutional Neural Network Approach”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 2128-33, doi:10.18517/ijaseit.9.6.9951.
Philippines is one of the world’s leading exporter of mangoes. The country produces many varieties of mangoes, one of which is the ‘Carabao’ mango. Several metric tons of mangoes are produced, and these have to be checked for defects before entering the market. With recent advances in technology, it has become efficient and relatively easy to use for these applications. The objective of this paper is to present a non-destructive method to check the quality of mangoes using computer vision (CV) and convolutional neural network (CNN) with a minimal number of samples. An experimental setup was created to simulate a production line. A webcam was used for capturing images of the mangoes, while a mini computer was used for controlling the peripherals. As basis for categorizing the mangoes as either good or bad, the Philippine National Standard (PNS) for mangoes was used. A basic background subtraction algorithm was used to extract the mango’s image. With these extracted images, a 2-category network was trained, and the achieved classification accuracy was 97.21%. The goal of having a high accuracy in classifying mangoes was achieved. There are multiple paths to explore in the future, including additional feature extraction methods, different neural networks, and hardware improvements, in order to speed up the sorting process. Moreover, it may be necessary to be able to identify mangoes with only slight defects to be used for other products, such as dried mangoes, to reduce product wastage.

K. Stark, V. Couto, and G. Gary, “The Philippines in the Mango Global Value Chain,” 2017.

PSA, “Major Fruit Crops Quarterly Bulletin,” Philippine Statistics Authority, 2019. [Online]. Available: http://www.psa.gov.ph/fruits-crops-bulletin/mango. [Accessed: 17-Jul-2019].

D. of T. and I. Philippines, “Philippine National Standard: Fresh Fruit - Mangoes,” vol. 2015, 2004.

L. Y. Chen et al., “Development of an electronic-nose system for fruit maturity and quality monitoring,” Proc. 4th IEEE Int. Conf. Appl. Syst. Innov. 2018, ICASI 2018, pp. 1129-1130, 2018.

Y. Hasegawa, A. L. Spetz, and D. Puglisi, “Ethylene gas sensor for evaluating postharvest ripening of fruit,” 2017 IEEE 6th Glob. Conf. Consum. Electron. GCCE 2017, vol. 2017-January, no. Gcce, pp. 1-4, 2017.

N. H. Hasanuddin et al., “Metal oxide based surface acoustic wave sensors for fruits maturity detection,” 2016 3rd Int. Conf. Electron. Des. ICED 2016, no. 1, pp. 52-55, 2017.

W. Lang and R. Jedermann, “What Can MEMS Do for Logistics of Food? Intelligent Container Technologies: A Review,” IEEE Sens. J., vol. 16, no. 18, pp. 6810-6818, 2016.

E. Vitzrabin and Y. Edan, “Changing Task Objectives for Improved Sweet Pepper Detection for Robotic Harvesting,” IEEE Robot. Autom. Lett., vol. 1, no. 1, pp. 578-584, 2016.

P. Leekul, S. Chivapreecha, C. Phongcharoenpanich, and M. Krairiksh, “Rician k-Factors-Based Sensor for Fruit Classification by Maturity Stage,” IEEE Sens. J., vol. 16, no. 17, pp. 6559-6565, 2016.

P. Leekul and M. Krairiksh, “A sensor for continuous fruit classification using Rician k-factor,” 2018 Int. Symp. Antennas Propag., pp. 1-2, 2018.

S. Bargoti and J. Underwood, “Deep fruit detection in orchards,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3626-3633.

S. Marimuthu and S. Mohamed Mansoor Roomi, “Particle Swarm Optimized Fuzzy Model for the Classification of Banana Ripeness,” IEEE Sens. J., vol. 17, no. 15, pp. 4903-4915, 2017.

Y. Polinar, K. F. Yaptenco, E. K. Peralta, and J. U. Agravante, “Near-infrared spectroscopy for non-destructive prediction of maturity and eating quality of ‘Carabao’ mango (Mangifera indica L.) fruit,” Agric. Eng. Int. CIGR J., vol. 21, no. 1, pp. 209-219, Apr. 2019.

K. Sujatha, R. S. Ponmagal, V. Srividhya, and T. Godhavari, “Feature extraction for ethylene gas measurement for ripening fruits,” Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 3804-3808, 2016.

C. S. Nandi, B. Tudu, and C. Koley, “A Machine Vision Technique for Grading of Harvested Mangoes Based on Maturity and Quality,” IEEE Sens. J., vol. 16, no. 16, pp. 6387-6396, Aug. 2016.

X. Liu et al., “Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association,” IEEE Robot. Autom. Lett., vol. 4, no. 3, pp. 2296-2303, Jul. 2019.

“Structure From Motion From Multiple Views.” [Online]. Available: https://www.mathworks.com/help/vision/examples/structure-from-motion-from-multiple-views.html. [Accessed: 20-Jul-2019].

X. Liu, D. Zhao, W. Jia, W. Ji, and Y. Sun, “A Detection Method for Apple Fruits Based on Color and Shape Features,” IEEE Access, vol. 7, pp. 67923-67933, 2019.

X. A. P. Calangian et al., “Vision-based Canopy Area Measurements,” in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018, pp. 1-4.

R. M. C. Santiago, S. L. Rabano, R. K. D. Billones, E. J. Calilung, E. Sybingco, and E. P. Dadios, “Insect detection and monitoring in stored grains using MFCCs and artificial neural network,” in TENCON 2017 - 2017 IEEE Region 10 Conference, 2017, pp. 2542-2547.

“Convolutional Neural Network - MATLAB & Simulink.” [Online]. Available: https://www.mathworks.com/solutions/deep-learning/convolutional-neural-network.html. [Accessed: 20-Jul-2019].

Chollet, F. (2015) Keras, GitHub. https://github.com/fchollet/keras

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).