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YOLOv10-LGDA: An Improved Algorithm for Defect Detection in Citrus Fruits Across Diverse Backgrounds.

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Summary

This study introduces YOLOv10-LGDA, an advanced AI model for detecting citrus diseases like black spot and canker. The improved model significantly enhances accuracy in identifying fruit defects, supporting citrus quality control.

Keywords:
LDConvYOLOv10citrus defectsobject detectionprecision crop protection

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Citrus fruit quality is compromised by diseases causing surface defects.
  • Accurate identification of diseases like citrus black spot, canker, greening, and melanose is crucial.

Purpose of the Study:

  • To develop an improved object detection method for accurate citrus disease identification.
  • To enhance detection capabilities for various citrus diseases, including those with subtle visual cues.

Main Methods:

  • An enhanced YOLOv10 model (YOLOv10-LGDA) was developed using LDConv for feature extraction.
  • Incorporated GFPN and AFPN modules for improved multi-scale feature fusion and detection.
  • Utilized DAT mechanism for efficiency and Slide Loss function to address sample imbalance.

Main Results:

  • YOLOv10-LGDA achieved high performance: 98.7% accuracy, 95.9% recall, 97.7% mAP@50, and 94% mAP@50:95.
  • Demonstrated significant improvements over the original YOLOv10 model (4.2%-4.5% increase in key metrics).
  • Outperformed other object detection algorithms in citrus disease recognition accuracy.

Conclusions:

  • The YOLOv10-LGDA model offers superior performance for citrus disease detection.
  • This advancement provides robust technical support for improving citrus fruit quality and industry sustainability.