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: | |
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| Format: | Final Year Project Report / IMRAD |
| Language: | en en |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2023
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| Subjects: | |
| 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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| Summary: | 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 |
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