A Hybrid Deep Multistacking Integrated Model for Plant Disease Detection
Plant disease detection is a critical challenge in agriculture, as undetected or poorly managed diseases can lead to significant yield losses, economic setbacks for farmers, and compromised food security. Therefore, accurate and efficient models for timely identification and mitigation are imperativ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11053793/ |
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| Summary: | Plant disease detection is a critical challenge in agriculture, as undetected or poorly managed diseases can lead to significant yield losses, economic setbacks for farmers, and compromised food security. Therefore, accurate and efficient models for timely identification and mitigation are imperative to address these challenges and ensure sustainable agricultural practices. In this study, we introduce a deep multistacking integrated model for plant leaf disease detection that leverages fine-tuned transfer learning (TL) models, multistacking feature generation, and an ensemble XGBoost meta-classifier. Our approach involves specialized pipelines for image preprocessing, augmentation, and fine-tuning TL models, resulting in a robust hybrid model. The multistacking feature generation technique aggregates the prediction probabilities from fine-tuned models, which are then utilized as input for the XGBoost classifier, boosting both accuracy and efficiency. We analyze the proposed model on three benchmark datasets: Tomato Disease Dataset (TDDS), Potato Pepper Dataset (PPDS), and Apple Grape Dataset (AGDS). Our experimental results demonstrate that the multistacking integrated model considerably outperforms traditional single-model methodologies, obtaining high accuracy scores of 99.78%, 99.86%, and 99.82% on TDDS, PPDS, and AGDS, respectively. These results underscore the value of our technique, particularly in multilevel classification, where combining data from several fine-tuned models allows for increased generalization and higher precision. By combining multistacking techniques and an ensemble XGBoost classifier, we have advanced the state-of-the-art in plant disease identification. Moreover, the model demonstrates improved computational efficiency, with lower build and forecast times compared to existing methodologies. These results show that combining advanced ensemble methods with well-tuned models can make plant disease detection systems more generic and reliable. Our work presents a scalable and effective strategy for enhancing plant disease identification, adding to the broader application of deep learning (DL) in precision agriculture. |
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| ISSN: | 2169-3536 |