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Nutsuda Jirajan. Classification of lime defects with convolutional neural network (CNN). Master's Degree(Logistics and Supply Chain Systems Engineering). Thammasat University. Thammasat University Library. : Thammasat University, 2022.
Classification of lime defects with convolutional neural network (CNN)
Name: Nutsuda Jirajan
keyword:
Computer vision
;
Lime
Abstract:
The defection of lime are been easily visible to classify by humans. But it required time-consuming and costly labor for the industry. Using computer vision is one of the approaches for determining the defection of lime since its less time-consuming and less expensive. The main purpose is to determine the defections of fresh market lime by utilizing color feature value, computer vision, and machine learning. In this project, using a convolutional neural network (CNN) classify lime into 2 classes ; defection (1) and healthy (0) since the CNN model is deep learning and the performance of the result is very high accuracy. The 5 hyperparameters for tuning the CNN model Epoch, Learning Rate, Decaying Rate, and Momentumare taken into account and designed using factorial methods to improve the model's performance. For the training, validation, and test models, respectively, the average performance of results by f1-score is 96.30%, 91.79%, and 85.92%
Thammasat University. Thammasat University Library
Address:
BANGKOK
Email:
preserv@tu.ac.th
Name: Pham, Duc Tai
Role:
advisor
Created:
2022
Modified:
2024-01-02
Issued:
2024-01-02
วิทยานิพนธ์/Thesis
application/pdf
eng
DegreeName: Master of Engineering
Level: Master's Degree
Descipline: Logistics and Supply Chain Systems Engineering
Grantor: Thammasat University
©copyrights Thammasat University
RightsAccess:
ใช้เวลา
0.024734 วินาที
Nutsuda Jirajan
| Title | Contributor | Type |
|---|---|---|
| Classification of lime defects with convolutional neural network (CNN)
มหาวิทยาลัยธรรมศาสตร์ Nutsuda Jirajan | Pham, Duc Tai | วิทยานิพนธ์/Thesis |
Pham, Duc Tai