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Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode.

Da Wang1, Rui Su1, Yanjie Xiong1

  • 1School of Engineering, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

A new automated system improves sugarcane seed cutting efficiency and quality. This technology utilizes AI for precise identification and cutting, reducing bud injury and enhancing planting practices.

Keywords:
YOLO V5computer visionpre-cutting modeprecision agriculturesugarcane

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Plant Science

Background:

  • Sugarcane is a vital cash crop in China, with pre-cutting planting being the primary method.
  • Current pre-cutting methods face challenges like low efficiency and poor cutting quality, impacting sugarcane yield.
  • Addressing these issues is crucial for advancing sugarcane cultivation technology.

Purpose of the Study:

  • To develop an automated sugarcane seed-cutting system to overcome limitations in traditional methods.
  • To enhance the efficiency and precision of sugarcane seed preparation for planting.
  • To improve the overall quality and success rate of sugarcane cultivation.

Main Methods:

  • Designed a sugarcane seed-cutting system integrating a cutting platform, seed-cutting device, visual inspection, and control system.
  • Employed the YOLO V5 network model within the visual inspection system for accurate sugarcane eustipe identification.
  • Incorporated a self-tensioning conveyor, crank slider, and high-speed rotary cutter for versatile seed processing.

Main Results:

  • Achieved a sugarcane seed recognition rate of at least 94.3% and an average accuracy of 98.2%.
  • Demonstrated a low bud injury rate, not exceeding 3.8%, ensuring seed viability.
  • Recorded an average cutting time of approximately 0.7 seconds per seed, indicating high operational efficiency.

Conclusions:

  • The developed automated system significantly improves sugarcane seed cutting rate and recognition accuracy.
  • The system's low bud injury rate and high efficiency offer substantial benefits for sugarcane planting.
  • This technology holds significant potential for advancing China's sugarcane pre-cutting planting mode and cultivation techniques.