Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi

The rapid progress in agriculture continually increases the demand for high-quality fruits. However, the quality of the fruits was usually assessed by a destructive method that normally led to a concern that this traditional approach is time-consuming and prone to human mistakes. To address these pr...

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Published in:2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
Main Author: Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142453203&doi=10.1109%2feSmarTA56775.2022.9935374&partnerID=40&md5=53ece948982f8d47cdc52f5cdeed9c9d
id 2-s2.0-85142453203
spelling 2-s2.0-85142453203
Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
2022
2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022


10.1109/eSmarTA56775.2022.9935374
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142453203&doi=10.1109%2feSmarTA56775.2022.9935374&partnerID=40&md5=53ece948982f8d47cdc52f5cdeed9c9d
The rapid progress in agriculture continually increases the demand for high-quality fruits. However, the quality of the fruits was usually assessed by a destructive method that normally led to a concern that this traditional approach is time-consuming and prone to human mistakes. To address these problems, an automatic non-destructive quality assessment of fruits using a Convolutional Neural Network (CNN) regression model based on images will be developed. Ficus Carica L. (figs) was used as a sample validation for the quality predictor. Three different stages (stage 1, stage 2, and stage 3) of fig fruit images were captured using a Pi camera and implemented in raspberry Pi3 model B+. The developed model will be used to predict the internal quality of fig fruits. In this paper, the quality of fig fruit was assessed in terms of its chemical properties which is the Brix sugar value (%). The model has been trained and validated to have a Root Mean Square Error (RMSE) of 1.38. Hence, the quality predictor is based on this prediction model and the performance was tested to give an RMSE of 0.99. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
spellingShingle Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
author_facet Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
author_sort Mazni I.A.; Setumin S.; Sunak Joseph A.A.; Khusairi Osman M.; Osman M.S.; Subri Tahir M.
title Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
title_short Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
title_full Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
title_fullStr Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
title_full_unstemmed Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
title_sort Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi
publishDate 2022
container_title 2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
container_volume
container_issue
doi_str_mv 10.1109/eSmarTA56775.2022.9935374
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142453203&doi=10.1109%2feSmarTA56775.2022.9935374&partnerID=40&md5=53ece948982f8d47cdc52f5cdeed9c9d
description The rapid progress in agriculture continually increases the demand for high-quality fruits. However, the quality of the fruits was usually assessed by a destructive method that normally led to a concern that this traditional approach is time-consuming and prone to human mistakes. To address these problems, an automatic non-destructive quality assessment of fruits using a Convolutional Neural Network (CNN) regression model based on images will be developed. Ficus Carica L. (figs) was used as a sample validation for the quality predictor. Three different stages (stage 1, stage 2, and stage 3) of fig fruit images were captured using a Pi camera and implemented in raspberry Pi3 model B+. The developed model will be used to predict the internal quality of fig fruits. In this paper, the quality of fig fruit was assessed in terms of its chemical properties which is the Brix sugar value (%). The model has been trained and validated to have a Root Mean Square Error (RMSE) of 1.38. Hence, the quality predictor is based on this prediction model and the performance was tested to give an RMSE of 0.99. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
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record_format scopus
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