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Sompol Boonyasuwanno. A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
Name: Teerapon Yodrot
Organization :
Rajamangala University of Technology Rattanakosin. Faculty of Industry and Technology
Email :
teerapon.yod@rmutr.ac.th
Name: Chalermkiat Sutacha
Organization :
Ubon Ratchathani Rajabhat University. Faculty of Industrial Technology
Email :
chalermkiat.s@ubru.ac.th
Name: Teerapong Orachon
Organization :
King Mongkut's Institute of Technology Ladkrabang. Department of Computer Engineering KOSEN
Email :
teerapong.or@kmitl.ac.th
Name: Nattapon Jangjongdee
Organization :
Rajamangala University of Technology Rattanakosin. Faculty of Industry and Technology
Email :
nattaponnonburi@gmail.com
Name: Sompol Boonyasuwanno
Organization :
Rajamangala University of Technology Rattanakosin. Faculty of Industry and Technology
Email :
sompol.boo@rmutr.ac.th
keyword:
Hybrid systems
LCSH:
Cucumber
-- Diseases and pests
LCSH:
Plant diseases
-- Diagnosis
LCSH:
Machine learning
Abstract:
This study investigates a hybrid machine learning
approach for the classification of cucumber leaf diseases,
combining the strengths of pre-trained Convolutional Neural
Networks (CNNs) and Support Vector Machines (SVM).
Focusing on a publicly available dataset, we evaluated four CNN
architecturesVGG16, ResNet50, AlexNet, and
EfficientNetV2for feature extraction, followed by SVM for
the final classification task. The performance of each model was
assessed using F1 scores and confusion matrices, with ResNet50
yielding the highest F1 score, indicative of a strong balance
between precision and recall. VGG16 and AlexNet showed
potential for improvement, which could be realized through
model tuning or integration into an ensemble framework.
EfficientNetV2, despite a slightly lower F1 score, highlighted the
importance of further training to enhance class distinction. The
research underlines the importance of a hybrid approach, which
harnesses deep learning's feature extraction capabilities with
SVM's classification strength, to improve the accuracy of
disease detection in cucumber plants
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-01-24
Issued:
2025-01-24
บทความ/Article
application/pdf
BibliograpyCitation :
In Rajamangala University of Technology Krungthep. 12th International Electrical Engineering Congress (iEECON 2024) (pp.313-316). Bangkok : Rajamangala University of Technology Krungthep
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.033947 วินาที
Teerapon Yodrot
| Title | Contributor | Type |
|---|---|---|
| Cucumber disease identification using multiple machine learning classifiers with a pre-trained VGG16 Model
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chalermkiat Sutacha;Teerapon Yodrot;Teerapong Orachon;Khwanjit Orkweha | บทความ/Article | |
| A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Teerapon Yodrot;Chalermkiat Sutacha;Teerapong Orachon;Nattapon Jangjongdee;Sompol Boonyasuwanno | บทความ/Article |
Chalermkiat Sutacha
| Title | Contributor | Type |
|---|---|---|
| Cucumber disease identification using multiple machine learning classifiers with a pre-trained VGG16 Model
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chalermkiat Sutacha;Teerapon Yodrot;Teerapong Orachon;Khwanjit Orkweha | บทความ/Article | |
| A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Teerapon Yodrot;Chalermkiat Sutacha;Teerapong Orachon;Nattapon Jangjongdee;Sompol Boonyasuwanno | บทความ/Article |
Teerapong Orachon
| Title | Contributor | Type |
|---|---|---|
| Cucumber disease identification using multiple machine learning classifiers with a pre-trained VGG16 Model
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chalermkiat Sutacha;Teerapon Yodrot;Teerapong Orachon;Khwanjit Orkweha | บทความ/Article | |
| A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Teerapon Yodrot;Chalermkiat Sutacha;Teerapong Orachon;Nattapon Jangjongdee;Sompol Boonyasuwanno | บทความ/Article |
Nattapon Jangjongdee
| Title | Contributor | Type |
|---|---|---|
| A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Teerapon Yodrot;Chalermkiat Sutacha;Teerapong Orachon;Nattapon Jangjongdee;Sompol Boonyasuwanno | บทความ/Article |
Sompol Boonyasuwanno
| Title | Contributor | Type |
|---|---|---|
| A study on the potency of hybrid models : detecting diseases in cucumber leaves with pre-trained CNNs and SVM
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Teerapon Yodrot;Chalermkiat Sutacha;Teerapong Orachon;Nattapon Jangjongdee;Sompol Boonyasuwanno | บทความ/Article |