Identification and Grading of Manage Using Image Processing

Fruit grading for commercialization is currently conducted through manual operations prone to inconsistent grading and human error, due to fatigue and the tedious nature of the task. Automation in agriculture especially for post-harvest yield inspection has played a vital role in reducing such error...

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Main Author: Shukor, Syazwan
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.23035 /
Published: Universiti Teknologi PETRONAS 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/23035/1/Copy%20of%20EE106_24666_Syazwan%20Bin%20Shukor.pdf
http://utpedia.utp.edu.my/23035/
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spelling utp-utpedia.230352022-03-11T04:18:55Z http://utpedia.utp.edu.my/23035/ Identification and Grading of Manage Using Image Processing Shukor, Syazwan TK Electrical engineering. Electronics Nuclear engineering Fruit grading for commercialization is currently conducted through manual operations prone to inconsistent grading and human error, due to fatigue and the tedious nature of the task. Automation in agriculture especially for post-harvest yield inspection has played a vital role in reducing such error and at the same time, ensuring produce such as fruits and vegetables are graded based on commercial standards. This project has developed an image processing algorithm for a systematic maturity identification of "Mangga Susu Thai Gold" mangos. The criteria of mangos to be assessed by the grading algorithm are color and weight. Classification of these mangos are conducted based on standards set by Federal Agriculture Marketing Authority (FAMA) mango ripeness index, Project activities have started using a proposed activity flow for algorithm development using Python and the experimental chamber setup for actual mange data collection. Actual mango data collection is focused on gamnering data such as mange weight, and skin color. Experimental chamber for image acquisition is developed in building the image dataset. 40 random samples of "Mangga Susu Gold Thai mangos are sampled. Features such as maximum colour component values, pixel area and perimeter are extracted using a feature extraction algorithm for compilation into separate "sv" files for classifier and prediction models training and testing. 3 classes are selected using silhouette analysis in labelling the mango features as training references for classifiers. Classification is conducted where a combination of LAB and SVM yielded best results (100% accuracy). Universiti Teknologi PETRONAS 2021-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/23035/1/Copy%20of%20EE106_24666_Syazwan%20Bin%20Shukor.pdf Shukor, Syazwan (2021) Identification and Grading of Manage Using Image Processing. Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Shukor, Syazwan
Identification and Grading of Manage Using Image Processing
description Fruit grading for commercialization is currently conducted through manual operations prone to inconsistent grading and human error, due to fatigue and the tedious nature of the task. Automation in agriculture especially for post-harvest yield inspection has played a vital role in reducing such error and at the same time, ensuring produce such as fruits and vegetables are graded based on commercial standards. This project has developed an image processing algorithm for a systematic maturity identification of "Mangga Susu Thai Gold" mangos. The criteria of mangos to be assessed by the grading algorithm are color and weight. Classification of these mangos are conducted based on standards set by Federal Agriculture Marketing Authority (FAMA) mango ripeness index, Project activities have started using a proposed activity flow for algorithm development using Python and the experimental chamber setup for actual mange data collection. Actual mango data collection is focused on gamnering data such as mange weight, and skin color. Experimental chamber for image acquisition is developed in building the image dataset. 40 random samples of "Mangga Susu Gold Thai mangos are sampled. Features such as maximum colour component values, pixel area and perimeter are extracted using a feature extraction algorithm for compilation into separate "sv" files for classifier and prediction models training and testing. 3 classes are selected using silhouette analysis in labelling the mango features as training references for classifiers. Classification is conducted where a combination of LAB and SVM yielded best results (100% accuracy).
format Final Year Project
author Shukor, Syazwan
author_sort Shukor, Syazwan
title Identification and Grading of Manage Using Image Processing
title_short Identification and Grading of Manage Using Image Processing
title_full Identification and Grading of Manage Using Image Processing
title_fullStr Identification and Grading of Manage Using Image Processing
title_full_unstemmed Identification and Grading of Manage Using Image Processing
title_sort identification and grading of manage using image processing
publisher Universiti Teknologi PETRONAS
publishDate 2021
url http://utpedia.utp.edu.my/23035/1/Copy%20of%20EE106_24666_Syazwan%20Bin%20Shukor.pdf
http://utpedia.utp.edu.my/23035/
_version_ 1741195898120044544
score 11.62408

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