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A Lightweight Edge-Deployable Framework for Intelligent Rice Disease Monitoring Based on Pruning and Distillation.

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This study introduces SCD-YOLOv11n, a lightweight AI model for detecting rice leaf diseases. It achieves high accuracy and speed, making it ideal for smart farming and digital agriculture applications.

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digital agricultureknowledge distillationlightweight YOLOnetwork pruningrice leaf disease detection

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Digital agriculture and smart farming necessitate efficient crop health monitoring.
  • Rice leaf diseases significantly impact crop yield, requiring accurate detection methods.
  • Field images present challenges like small lesions, varying light, and background clutter.

Purpose of the Study:

  • To develop a lightweight object detection model for identifying rice leaf diseases.
  • To balance detection accuracy with computational efficiency for practical agricultural applications.
  • To address the challenges of small, multi-scale lesions and complex image conditions.

Main Methods:

  • Investigated SCD-YOLOv11n, a lightweight detector featuring a StarNet backbone and C3k2-Star module for multi-scale feature extraction.
  • Introduced a Detail-Strengthened Cross-scale Detection (DSCD) head to enhance localization of small lesions.
  • Employed DepGraph-based mixed group-normalization pruning and channel-wise feature distillation for model compression.

Main Results:

  • The compressed SCD-YOLOv11n model requires only 1.9 MB of storage.
  • Achieved high detection performance with 97.4% mAP@50 and 76.2% mAP@50:95.
  • Demonstrated a fast processing speed of 184 FPS under tested conditions.

Conclusions:

  • SCD-YOLOv11n offers a viable solution for accurate and computationally efficient rice disease monitoring.
  • The model's lightweight design is suitable for deployment in digital agriculture and smart farming systems.
  • Provides a quantitative benchmark for developing efficient object detectors in agricultural contexts.