Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification
This is a challenge to identify tomato plant diseases using naked eyes. In implementing an automation plantation system, a plant health classification system is necessary in monitoring the health of the plants. This study proposes a system that can classify tomato plant health into five categories o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11556/1/J16084_6c08d828d34b1a281bdc492009add2c0.pdf http://eprints.uthm.edu.my/11556/ https://doi.org/10.1134/S1054661823010017 |
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| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | This is a challenge to identify tomato plant diseases using naked eyes. In implementing an automation plantation system, a plant health classification system is necessary in monitoring the health of the plants. This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. Five different pre-trained deep learning algorithms (i.e. Resnet-50, AlexNet, GoogleNet, VGG16, and VGG19) were studied and compared. A Raspberry Pi coupled with a camera was proposed to
capture tomato plant leaf image. After that, a support vector machine (SVM) with the extracted features was trained for the plant health classification. The results indicate that SVM coupled with ResNet-50 was the best with averaged training and testing accuracies of 98.26 and 93.33%, respectively |
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