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FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING

In this study, we present a fruit recognition system using machine learning techniques. The system consists of several distinct phases, including image acquisition, image pre-processing, feature extraction, classification, and results and evaluation. In the image acquisition phase, the system...

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Main Author: LOH, WENG KEONG
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
QA76 Computer software
Online Access:http://ir.unimas.my/id/eprint/44164/1/LOH%20WENG%20KEONG%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44164/2/LOH%20WENG%20KEONG%20%20ft.pdf
http://ir.unimas.my/id/eprint/44164/
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spelling my.unimas.ir.441642024-01-17T04:34:32Z http://ir.unimas.my/id/eprint/44164/ FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING LOH, WENG KEONG QA76 Computer software In this study, we present a fruit recognition system using machine learning techniques. The system consists of several distinct phases, including image acquisition, image pre-processing, feature extraction, classification, and results and evaluation. In the image acquisition phase, the system gathers a dataset of images that will be used for training and testing. These images may be acquired from a variety of sources, such as digital cameras or online databases. In the image pre-processing phase, the system prepares the images for further analysis by cleaning them up and standardizing them. This may include tasks such as removing noise, correcting for distortion, or resizing the images. In the feature extraction phase, the system extracts meaningful features from the images that can be used to distinguish one fruit from another. These features include color and shape features. In the classification phase, the system uses the extracted features to classify the fruits using a machine learning algorithm – a backpropagation neural network (BPNN). Finally, in the results and evaluation phase, the system assesses the accuracy and effectiveness of the classification process. Next, a fruit recognition prototype is developed to implement the trained model. The prototype is ready to use to recognize input image and display the result. Overall, our approach demonstrates a thorough and systematic approach to developing a fruit recognition system using machine learning techniques Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44164/1/LOH%20WENG%20KEONG%20%20%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/44164/2/LOH%20WENG%20KEONG%20%20ft.pdf LOH, WENG KEONG (2023) FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
LOH, WENG KEONG
FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
description In this study, we present a fruit recognition system using machine learning techniques. The system consists of several distinct phases, including image acquisition, image pre-processing, feature extraction, classification, and results and evaluation. In the image acquisition phase, the system gathers a dataset of images that will be used for training and testing. These images may be acquired from a variety of sources, such as digital cameras or online databases. In the image pre-processing phase, the system prepares the images for further analysis by cleaning them up and standardizing them. This may include tasks such as removing noise, correcting for distortion, or resizing the images. In the feature extraction phase, the system extracts meaningful features from the images that can be used to distinguish one fruit from another. These features include color and shape features. In the classification phase, the system uses the extracted features to classify the fruits using a machine learning algorithm – a backpropagation neural network (BPNN). Finally, in the results and evaluation phase, the system assesses the accuracy and effectiveness of the classification process. Next, a fruit recognition prototype is developed to implement the trained model. The prototype is ready to use to recognize input image and display the result. Overall, our approach demonstrates a thorough and systematic approach to developing a fruit recognition system using machine learning techniques
format Final Year Project Report
author LOH, WENG KEONG
author_facet LOH, WENG KEONG
author_sort LOH, WENG KEONG
title FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
title_short FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
title_full FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
title_fullStr FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
title_full_unstemmed FRUIT RECOGNITION APPLICATION USING MACHINE LEARNING
title_sort fruit recognition application using machine learning
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44164/1/LOH%20WENG%20KEONG%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44164/2/LOH%20WENG%20KEONG%20%20ft.pdf
http://ir.unimas.my/id/eprint/44164/
_version_ 1789430368995115008
score 13.211869

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