Quality assessment of mangoes using convolutional neural network

Philippines is one of the countries in the world known for exporting good quality crops. Mangoes in the Philippines are very popular for its good sweet taste and considerably one of the best. Hence, ensuring the quality of the crop to be exported is essential. The study focused on utilizing convolut...

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Main Authors: Puno, John Carlo V., Billones, Robert Kerwin D., Bandala, Argel A., Dadios, Elmer P., Calilune, Edwin J., Joaquin, Arlene C.
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Published: Animo Repository 2019
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3017
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-40162021-11-19T07:30:42Z Quality assessment of mangoes using convolutional neural network Puno, John Carlo V. Billones, Robert Kerwin D. Bandala, Argel A. Dadios, Elmer P. Calilune, Edwin J. Joaquin, Arlene C. Philippines is one of the countries in the world known for exporting good quality crops. Mangoes in the Philippines are very popular for its good sweet taste and considerably one of the best. Hence, ensuring the quality of the crop to be exported is essential. The study focused on utilizing convolutional neural network in determining the quality of carabao mango (Mangifera Indica). To make sure that all sides of the mango is going to be considered for the quality assessment, a mechanical system that uses conveyor belt, rollers, and camera was used to gather videos for training and validation of the model. The videos were extracted into frames and gone through image processing to remove the background and retain the mango only. The dataset is composed of different mangoes having both good and bad qualities. The implemented model used a total of 5550 training samples with 94.99% accuracy and a total of 2320 samples used for validation with an accuracy of 97.21%. © 2019 IEEE. 2019-11-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/3017 info:doi/10.1109/CIS-RAM47153.2019.9095789 Faculty Research Work Animo Repository Mango—Grading--Philippines Mango—Philippines—Quality control Image processing Neural networks (Computer science) Electrical and Electronics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Mango—Grading--Philippines
Mango—Philippines—Quality control
Image processing
Neural networks (Computer science)
Electrical and Electronics
spellingShingle Mango—Grading--Philippines
Mango—Philippines—Quality control
Image processing
Neural networks (Computer science)
Electrical and Electronics
Puno, John Carlo V.
Billones, Robert Kerwin D.
Bandala, Argel A.
Dadios, Elmer P.
Calilune, Edwin J.
Joaquin, Arlene C.
Quality assessment of mangoes using convolutional neural network
description Philippines is one of the countries in the world known for exporting good quality crops. Mangoes in the Philippines are very popular for its good sweet taste and considerably one of the best. Hence, ensuring the quality of the crop to be exported is essential. The study focused on utilizing convolutional neural network in determining the quality of carabao mango (Mangifera Indica). To make sure that all sides of the mango is going to be considered for the quality assessment, a mechanical system that uses conveyor belt, rollers, and camera was used to gather videos for training and validation of the model. The videos were extracted into frames and gone through image processing to remove the background and retain the mango only. The dataset is composed of different mangoes having both good and bad qualities. The implemented model used a total of 5550 training samples with 94.99% accuracy and a total of 2320 samples used for validation with an accuracy of 97.21%. © 2019 IEEE.
format text
author Puno, John Carlo V.
Billones, Robert Kerwin D.
Bandala, Argel A.
Dadios, Elmer P.
Calilune, Edwin J.
Joaquin, Arlene C.
author_facet Puno, John Carlo V.
Billones, Robert Kerwin D.
Bandala, Argel A.
Dadios, Elmer P.
Calilune, Edwin J.
Joaquin, Arlene C.
author_sort Puno, John Carlo V.
title Quality assessment of mangoes using convolutional neural network
title_short Quality assessment of mangoes using convolutional neural network
title_full Quality assessment of mangoes using convolutional neural network
title_fullStr Quality assessment of mangoes using convolutional neural network
title_full_unstemmed Quality assessment of mangoes using convolutional neural network
title_sort quality assessment of mangoes using convolutional neural network
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/3017
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