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LSD-YOLO: Enhanced YOLOv8n Algorithm for Efficient Detection of Lemon Surface Diseases.

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  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.

Plants (Basel, Switzerland)
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A new model, lemon surface disease YOLO (LSD-YOLO), accurately detects lemon diseases using advanced convolutional techniques. This contributes to improved lemon quality and yield by enabling early disease identification.

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YOLOv8attention mechanismslemon diseaseobject detectionsmall objects

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Lemon cultivation is vital globally, but diseases significantly reduce yield and quality.
  • Early detection of lemon diseases is crucial for effective management and crop preservation.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for accurate and early detection of lemon diseases.
  • To enhance feature extraction and multi-scale feature fusion for improved disease recognition.

Main Methods:

  • A comprehensive dataset of 2022 lemon images (healthy and diseased) was curated.
  • A novel model, lemon surface disease YOLO (LSD-YOLO), was proposed, integrating Switchable Atrous Convolution (SAConv) and Convolutional Block Attention Module (CBAM).
  • The LSD-YOLO model incorporates C2f-SAC and a small-target detection layer for enhanced feature processing.

Main Results:

  • The LSD-YOLO model achieved an accuracy of 90.62% and mAP@50-95 of 80.84% on the lemon disease dataset.
  • The model demonstrated improved performance compared to the original YOLOv8n, particularly in mAP@50 and mAP@50-95 metrics.
  • Enhanced feature extraction and multi-scale fusion contributed to superior detection capabilities.

Conclusions:

  • The proposed LSD-YOLO model offers a highly accurate solution for identifying healthy and diseased lemons.
  • This advancement effectively addresses the challenge of lemon disease detection, supporting agricultural productivity.
  • The study highlights the potential of integrating advanced convolutional modules for agricultural disease diagnostics.