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A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.

Eftichia Badeka1, Eleftherios Karapatzak2, Aikaterini Karampatea2

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Summary
This summary is machine-generated.

This study developed a YOLO v7 algorithm for estimating grape maturity in Assyrtiko vineyards. The AI accurately detects five maturity stages, advancing autonomous grapevine management.

Keywords:
YOLOgrape maturity detectionmaturity estimationobject detection

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Viticulture

Background:

  • Robots are increasingly used in viticulture to enhance productivity and precision.
  • Labor shortages and high costs drive the need for automated vineyard operations.

Purpose of the Study:

  • To develop an algorithm for grape maturity estimation in vineyard management.
  • To improve automated decision-making for grape harvesting and quality control.

Main Methods:

  • Development of an object detection algorithm using You Only Look Once (YOLO) v7.
  • Training the algorithm with images of Assyrtiko grapes over six weeks in Drama, Greece.
  • Comparing YOLO v7 performance against alternative object detection architectures.

Main Results:

  • The YOLO v7 algorithm successfully detected five distinct grape maturity stages.
  • The proposed algorithm demonstrated superior precision and accuracy compared to other methods.
  • High-quality images were utilized for robust algorithm validation.

Conclusions:

  • The developed algorithm enables accurate, automated grape maturity estimation.
  • This research supports the advancement of autonomous robots for comprehensive grapevine management.
  • The findings have significant implications for optimizing viticulture practices.