An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection

Abstract

The early and accurate detection of plant diseases is critical for sustainable agriculture, ensuring crop health, reducing losses, and supporting food security. To address this challenge, we present an Ensemble Learning Framework with Explainable AI (XAI) tailored to disease detection, using cucumber leaf diagnosis as a key use case. In this study, we experimented with a dataset comprising 6,400 images capturing six prevalent cucumber leaf diseases – Gummy Stem Blight, Downy Mildew, Anthracnose, Bacterial Wilt, Belly Rot, and Pythium Fruit Rot – alongside two healthy categories. Prior to training, the images underwent preprocessing steps such as resizing, rescaling, and data augmentation (through random rotations, flips, zooms, and contrast adjustments) to enhance model generalization. The proposed framework unites multiple architectures – CNN, DenseNet121, EfficientNetB0, InceptionV3, MobileNetV2, ResNet50, and Xception – into an ensemble that attained overall accuracy of 99%, alongside high recall and F1-scores. Individual models demonstrated accuracy ranging from 88.71% to 99%, underscoring the robustness of the ensemble. Integrating XAI methods further ensures interpretable outputs, granting valuable insights into the decision-making process and heightening transparency for researchers and agronomists. The findings confirm that transfer learning, model ensembling, and interpretability methods significantly enhance classification performance, especially in cases of limited data, offering a scalable solution for improved disease management in agriculture. Additionally, the framework is scalable for real-world deployment by enabling real-time disease monitoring on edge devices (e.g., Raspberry Pi, IoT systems), seamless integration with smart farming platforms, and continuous learning for adaptive crop management.