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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Ripe Tomato Detection Algorithm Based on Improved YOLOv9.

Yan Wang1, Qianjie Rong1, Chunhua Hu1

  • 1College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.

Plants (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv9-C model for accurate ripe tomato recognition. The improved model offers higher precision and recall rates, making tomato picking more efficient.

Keywords:
HGBlockSPD-ADownYOLOv9fruit detectionripe tomatoes

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Machine learning

Background:

  • Accurate ripe tomato recognition is vital for efficient harvesting.
  • Existing fruit detection algorithms face challenges with small object detection in complex environments.

Purpose of the Study:

  • To develop an enhanced YOLOv9-C model for improved ripe tomato detection.
  • To address limitations in identifying tomatoes and detecting small objects accurately.

Main Methods:

  • Collected tomato data and applied Mosaic data augmentation for robustness.
  • Integrated HGBlock and SPD-ADown modules into the YOLOv9-C architecture for feature extraction and down-sampling.
  • Evaluated the enhanced model's performance against the original YOLOv9 and RetinaNet.

Main Results:

  • The enhanced YOLOv9-C model achieved precision of 97.2% and recall of 92.3%.
  • It demonstrated a 1.3% increase in accuracy and 1.1% in recall compared to the original model.
  • Achieved a mean average precision (mAP@0.5) of 98%, outperforming RetinaNet by 9.6% with a faster inference time of 14.7 ms.

Conclusions:

  • The enhanced YOLOv9-C model provides a reliable and efficient technique for ripe tomato recognition.
  • Its superior speed and accuracy make it suitable for practical applications in automated tomato picking.
  • The integration of HGBlock and SPD-ADown modules significantly boosts detection performance.