A Deep Learning Based Fusion Model For Plant Leaf Disease Classification

Ishaq M. H., DİREKOĞLU C.

7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025, Ankara, Turkey, 23 - 24 May 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ichora65333.2025.11017095
  • City: Ankara
  • Country: Turkey
  • Keywords: Deep learning, Disease Classification, Fusion model, Leaf disease in plants
  • Middle East Technical University Affiliated: Yes

Abstract

Agriculture is the backbone of a country's economy. For agricultural sustainability, it is important to accurately and early diagnose if a plant is infected by any disease in order to protect the plant. This paper proposes a deep learning-based fusion model for plant leaf disease classification. Using pre-trained models (ResNet50, VGG16, and EfficientNetB0), the features extracted from images are concatenated and then input into a deep neural network for classification. The proposed work utilizes the publicly available PlantVillage dataset from Kaggle for the leaf disease classification. In this study, 8 balanced classes were used. The proposed fusion model achieves the highest classification test accuracy of 98.25%, with training 99.15%, a loss of 0.0315, and an average 5-fold cross-validation accuracy of 98.7625%. The proposed model is effective and can contribute to agricultural sustainability.