Mehdhar S. A. M. Al-gaashani

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

• Authors Details :  
• Mehdhar S. A. M. Al-gaashani

Journal title : IET Image Processing

Publisher : Institution of Engineering and Technology (IET)

Online ISSN : 1751-9667

Page Number : 913-925

Journal volume : 16

Journal issue : 3

134 Views Original Article

Tomato is one of the most important vegetables worldwide. It is considered a mainstayof many countries’ economies. However, tomato crops are vulnerable to many diseasesthat lead to reducing or destroying production, and for this reason, early and accuratediagnosis of tomato diseases is very urgent. For this reason, many deep learning modelshave been developed to automate tomato leaf disease classification. Deep learning isfar superior to traditional machine learning with loads of data, but traditional machinelearning may outperform deep learning for limited training data. The authors proposea tomato leaf disease classification method by exploiting transfer learning and featuresconcatenation. The authors extract features using pre-trained kernels (weights) fromMobileNetV2 and NASNetMobile; then, they concatenate and reduce the dimensionalityof these features using kernel principal component analysis. Following that, they feedthese features into a conventional learning algorithm. The experimental results confirmthe effectiveness of concatenated features for boosting the performance of classifiers.The authors have evaluated the three most popular traditional machine learning classifiers,random forest, support vector machine, and multinomial logistic regression; amongthem, multinomial logistic regression achieved the best performance with an averageaccuracy of 97%.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1049/ipr2.12397

Article Subject Details

• Artificial Intelligence
• Computer Science
• Computer Science Applications

Article Keywords Details

• DL
• ML
• Tomato leaf disease
• Transfer learning

Article File

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Article References

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