Benchmarking the LGBM, Random Forest, and XGBoost Models Based on Accuracy in Classifying Melon Leaf Disease

Leaf diseases in melon plants cause losses for melon farmers. However, melon plants become less productive or even die. Downy mildew is a foliar disease that spreads rapidly in melon plants. Determining the level of downy mildew in melon leaves is important. Determining the level of downy mildew disease, farmers can carry out preventive treatment according to the severity level of downy mildew disease. This study aimed to create a classification model for the level of downy mildew disease on melon leaves using combined features and to compare the classification models, namely the LGBM, Random Forest, and XGBoost models. The combined features consist of colour, texture, Shannon entropy, and Canny edge features. The combined features are used as input for a classification model to predict the level of downy mildew leaf disease in melon plants. Model evaluation was carried out with three scenarios of data sharing: the first scenario, 90% training data and 10% test data; the second scenario, 80% training data and 20% test data; and the third scenario, 70% training data and 30% test data. The results of the evaluation of the model with the confusion matrix show that for the first and second scenarios, the highest accuracy was achieved by the Random Forest algorithm, with 72% and 73% accuracy, respectively. For the third scenario, the highest accuracy was obtained using the XGBoost algorithm.

@article{Rozikin2023,
  title     = {Benchmarking the LGBM, Random Forest, and XGBoost Models Based on Accuracy in Classifying Melon Leaf Disease},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Chaerur Rozikin and Agus Buono and Sri Wahjuni and Chusnul Arif and Widodo},
  doi       = {10.14569/IJACSA.2023.0141022},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141022}
}