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February 24, 2026Discover Artificial IntelligenceOpen Access

Yolo driven computer vision framework for paddy disease

Santosh Kumar Sumit Kumar Durgesh Nandan

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Key Points

This research aims to enhance paddy disease detection using an optimized YOLOv8 framework.
Optimized YOLOv8 with Paddy-Adaptive Loss Function for paddy disease detection.
Implemented 5-fold cross-validation for model validation.
Used a Hierarchical Feature Fusion Network for feature aggregation.
Conducted tests on the Paddy Doctor dataset with 10,407 images.
Achieved 98% accuracy, a 5.5% improvement over the baseline YOLOv8.
Receptive field increased by 34% with the proposed framework.
Demonstrated 15 ms end-to-end processing time for optimized models.

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Implication

Proposed YOLOv8 optimizations improve paddy disease detection accuracy by 5.5% in edge devices, indicating advancements in agricultural AI.

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Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/699f956d1bc9fecf3dab339c https://doi.org/10.1007/s44163-026-00945-6

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