An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection
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 cucumbe...
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Elsevier
2025-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259000562500013X |
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| author | Mohammad Rifat Ahmmad Rashid Md. AL Ehtesum Korim Mahamudul Hasan Md Sawkat Ali Mohammad Manzurul Islam Taskeed Jabid Raihan Ul Islam Maheen Islam |
| author_facet | Mohammad Rifat Ahmmad Rashid Md. AL Ehtesum Korim Mahamudul Hasan Md Sawkat Ali Mohammad Manzurul Islam Taskeed Jabid Raihan Ul Islam Maheen Islam |
| author_sort | Mohammad Rifat Ahmmad Rashid |
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| description | 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. |
| format | Article |
| id | doaj-art-a8bd9e059f6f4306a0c20ef9fbc9fadd |
| institution | DOAJ |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| spelling | doaj-art-a8bd9e059f6f4306a0c20ef9fbc9fadd2025-08-20T03:21:38ZengElsevierArray2590-00562025-07-012610038610.1016/j.array.2025.100386An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detectionMohammad Rifat Ahmmad Rashid0Md. AL Ehtesum Korim1Mahamudul Hasan2Md Sawkat Ali3Mohammad Manzurul Islam4Taskeed Jabid5Raihan Ul Islam6Maheen Islam7Corresponding author.; Department of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshDepartment of Computer Science And Engineering, East West University, A/2 Jahurul Islam Ave, Dhaka, 1212, BangladeshThe 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.http://www.sciencedirect.com/science/article/pii/S259000562500013XLeaf diseasesDeep learningTransfer learningConvolutional neural networks (CNNs)Cucumber disease detectionMachine learning |
| spellingShingle | Mohammad Rifat Ahmmad Rashid Md. AL Ehtesum Korim Mahamudul Hasan Md Sawkat Ali Mohammad Manzurul Islam Taskeed Jabid Raihan Ul Islam Maheen Islam An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection Array Leaf diseases Deep learning Transfer learning Convolutional neural networks (CNNs) Cucumber disease detection Machine learning |
| title | An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection |
| title_full | An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection |
| title_fullStr | An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection |
| title_full_unstemmed | An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection |
| title_short | An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection |
| title_sort | ensemble learning framework with explainable ai for interpretable leaf disease detection |
| topic | Leaf diseases Deep learning Transfer learning Convolutional neural networks (CNNs) Cucumber disease detection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S259000562500013X |
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