Comparative Analysis of SVM and Decision Tree Classifiers for Grape Leaf Disease Classification

DOI: 10.56815/IJEMS.V3.I2.2023/8-13

Authors

  • Rahul S. Pachade Research scholar, Madhyanchal Professional University, Ratibad, Bhopal, (MP), India
  • Avinash Sharma Professor, Department of Computer Science and Engineering, Madhyanchal Professional University, Ratibad, Bhopal, (MP), India
  • Manoj Patil Assistant professor, Department of Computer Engineering, DMCE, Mumbai, (MS), India

Keywords:

Support vector machine, Decision tree, Grape leaf diseases, Precision, F1-score

Abstract

This research paper presents an in-depth analysis of the performance of Support Vector Machine (SVM) and Decision Tree classifiers in
classifying grape leaf diseases. The study utilizes the grape leaf disease dataset and evaluates the classifiers based on precision, recall, F1-score, and overall accuracy. The results demonstrate outstanding performance by both classifiers, with perfect accuracy scores of 1.0. The classifiers exhibit high precision, recall, and F1-scores across the majority of disease categories, indicating their proficiency in accurately identifying grape leaf diseases. The findings highlight the effectiveness of these classifiers as valuable tools for disease classification. However, further evaluations and considerations are recommended to ensure their robustness and dependability in real-world scenarios.

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Published

2023-12-10

Issue

Vol. (2023) No. 02

Section

Articles