PlantHealthNet: Transformer-Enhanced Hybrid Models for Disease Diagnosis and Severity Estimation in Agriculture

Global plant diseases represent a major threat to agriculture and represent significant economic losses constituting an important threat to food security. This study introduces a transformative hybrid framework for plant disease diagnosis and severity estimation, combining the strengths of advanced...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 101160 - 101176
Main Author: Iqbal, Abid
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Agriculture
Artificial intelligence
Computational modeling
Crops
Deep learning
detection transformer (DETR)
Diagnosis
disease severity estimation
Diseases
Economic impact
Economics
Encoders-Decoders
Farming
Machine learning
Medical diagnosis
plant disease classification
plant disease segmentation
Plant diseases
precision agriculture
Real-time systems
SAM2-UNet
Segmentation
sustainable agriculture
Swin transformer
Transformers
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Global plant diseases represent a major threat to agriculture and represent significant economic losses constituting an important threat to food security. This study introduces a transformative hybrid framework for plant disease diagnosis and severity estimation, combining the strengths of advanced deep learning architectures: Detection Transformer (DETR), Swin Transformer and SAM2-UNet. The proposed methodology incorporates classification, detection, segmentation, and severity estimation in the form of a unified pipeline to address the complexities of real-world agricultural settings, including variable lighting, background variety and large-scale fields. With a hierarchical attention mechanism for efficient feature extraction, the Swin Transformer achieves a top 1 accuracy of 85.6%, and a top 5 accuracy of 96.2%. Utilizing its encoder-decoder attention technique, Detection Transformer (DETR) effectively detects diseases by employing an encoder to forecast bounding boxes and class labels for affected areas. Segmentation performance is enhanced with SAM2-UNet, achieving a pixel-level segmentation with a Dice Similarity Coefficient (DSC) of 94.7%, cleanly delineating disease-affected areas. Finally, disease impact is quantified in the final severity prediction stage to allow for targeted interventions based on disease intensity. The framework has been extensively validated through experiments, and it performs significantly better on all the evaluation metrics: Average ROC AUC of 99.97%, Average Precision-Recall AUC of 98.47%, and F1-score of 94.46%. The suggested system improves precision agriculture by making disease detection and management reliable, scalable, and efficient. This work applies cutting-edge deep learning to sustainable agriculture to reduce crop losses, optimise resource use, and provide farmers with proactive disease management knowledge.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3576990