Detection and Classification of Tomato Leaf Diseases Using DWT Algorithm with other Algorithms

Authors

  • Eman Bashir Alghwil Internet Technologies Department, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya
  • Aisha Moftah Eghwila Internet Technologies Department, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya
  • Mohamed Ahmed Sullabi Information Technology Department, School of Engineering and Applied Science, Libyan Academy, Misurata, Libya

Keywords:

SVM, DWT, PCA, Detection, Classification, Tomato Leaves, Diseases

Abstract

One of the most efficient strategies to stop the tomato plant disease's progress is to detect tomato plant infections. In this research, it was research to use the Principal Component Analysis (PCA) and Discrete Wave Transformation (DWT) algorithms to extract the characteristics of tomato leaf images from a database compiled from the (Kaggel) consisting of 1000 images. These images were divided into ten categories depending on the type of the disease: bacterial spot, target spot, mosaic virus, late blight, leaf rot, yellow leaf curl virus, spider spot mites, early blight, spot Septoria virus, and healthy. Where Support Vector Machine technology is used to categorize these attributes. where 70% of them were used for training, and 30% for testing. The final results of the accuracy obtained from the experiments of using the research mode is (92.33%).

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Published

2025-10-07

How to Cite

Eman Bashir Alghwil, Aisha Moftah Eghwila, & Mohamed Ahmed Sullabi. (2025). Detection and Classification of Tomato Leaf Diseases Using DWT Algorithm with other Algorithms. North African Journal of Scientific Publishing (NAJSP), 3(4), 11–17. Retrieved from https://najsp.com/index.php/home/article/view/632

Issue

Volume 3, Issue 4, 2025

Section

محور العلوم التطبيقية والطبيعية

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.