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The application of Machine Learning (ML) algorithms has become widespread in solving diverse data classification challenges, extending to domains like agriculture, notably in tasks like plant disease detection. These classification tasks, especially in agriculture, often involve high-dimensional datasets, prompting researchers to seek efficient solutions. However, such problems pose significant computational challenges due to the vast number of variable combinations. Support Vector Machine (SVM), known for its efficacy with high-dimensional datasets and numerical features commonly found in plant disease detection, emerges as a promising solution. While SVM demonstrates competence with default settings, its performance can be notably enhanced through parameter optimization techniques like grid search and cross-validation (CV). This study aims the optimization of the main hyperparameters of SVM and the extraction of the best parameters, in this case, the optimal parameters lead to satisfaction, where, the average values of 79.17, 80.24, 79.17, and 79.57%, were obtained for Testing Accuracy (TA), Precision, Recall, and F1-Score respectively.

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Optimization of Hyperparameters for SVM Classification of Citrus Diseases Using Grid Search and Cross-Validation

  • 会议论文

  • 发表日期：

    2025年

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  • Hanae Al Kaddouri,
  • Jalal Blaacha,
  • Hajar Hamdaoui,
  • Abdelmalek El Mehdi,
  • Youssef Douzi,
  • Hind Messbah,
  • Yassine Zarrouk

摘要

The application of Machine Learning (ML) algorithms has become widespread in solving diverse data classification challenges, extending to domains like agriculture, notably in tasks like plant disease detection. These classification tasks, especially in agriculture, often involve high-dimensional datasets, prompting researchers to seek efficient solutions. However, such problems pose significant computational challenges due to the vast number of variable combinations. Support Vector Machine (SVM), known for its efficacy with high-dimensional datasets and numerical features commonly found in plant disease detection, emerges as a promising solution. While SVM demonstrates competence with default settings, its performance can be notably enhanced through parameter optimization techniques like grid search and cross-validation (CV). This study aims the optimization of the main hyperparameters of SVM and the extraction of the best parameters, in this case, the optimal parameters lead to satisfaction, where, the average values of 79.17, 80.24, 79.17, and 79.57%, were obtained for Testing Accuracy (TA), Precision, Recall, and F1-Score respectively.