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Variable rate air-assisted spray based on real-time disease spot identification.

Jinhui Zhang1, Hao Yin1, Liangfu Zhou2

  • 1College of Engineering, Nanjing Agricultural University, Nanjing, China.

Pest Management Science
|September 29, 2022
PubMed
Summary

This study introduces a variable-rate application (VA) system using deep learning to detect disease spots on pear trees, significantly reducing pesticide use by 51.9% compared to traditional methods.

Keywords:
complex scenarioscontrol strategiesdeep convolutional neural network modelsdisease spot detectionvariable rate application systems

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Area of Science:

  • Agricultural Engineering
  • Plant Pathology
  • Computer Vision

Background:

  • Current variable-rate application (VA) of agrochemicals relies on canopy volume and biomass.
  • Canopy characteristics correlate with disease incidence and resistance in fruit trees.
  • Existing methods lack precision in targeting diseased areas.

Purpose of the Study:

  • To develop a variable-rate application (VA) system for fruit trees using deep convolutional neural networks.
  • To enable real-time recognition and targeted application of agrochemicals based on disease spot identification.
  • To evaluate the system's performance and pesticide-saving potential.

Main Methods:

  • Implemented deep convolutional neural networks for real-time disease spot recognition on pear trees.
  • Conducted field performance tests to validate the VA system's efficacy.
  • Specified limitations and application scenarios for the disease spot recognition technology.

Main Results:

  • Achieved a mean average precision of 87.42%, precision of 83.76%, and recall of 87.23% for spot recognition.
  • Demonstrated an 81.3% spot recognition rate under specific conditions (1.2m distance, 4-8mm spot diameter, 55.76% porosity).
  • The VA system reduced spray volume by 51.9% compared to conventional methods while maintaining application quality.

Conclusions:

  • The proposed VA technology, based on real-time disease spot identification, effectively reduces pesticide application in non-diseased areas.
  • This approach moves away from saturation application practices, leading to significant reductions in overall pesticide use.
  • The system offers a more precise and sustainable alternative to traditional constant-rate agrochemical application models.