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A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.

Yufei Liu1, Jingxin Liu2, Wei Cheng1

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

Plants (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic pruning method for automatic plant disease detection, even on low-power devices. The model achieves 94% accuracy, offering a practical solution for diverse computing environments.

Keywords:
deep learningdynamic pruninglow-computing-platform friendlyre-parameterization

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate and timely plant disease detection is vital for agriculture.
  • Existing methods often require high computational resources, limiting their use in low-computing environments.
  • Need for efficient and adaptable disease detection models.

Purpose of the Study:

  • To propose a dynamic-pruning-based method for automatic plant disease detection.
  • To enable plant disease detection in low-computing situations.
  • To develop a model adaptable to hardware platforms with varying computational power.

Main Methods:

  • Collected datasets spanning four crops and 12 diseases over three years.
  • Proposed a re-parameterization method to enhance convolutional neural network (CNN) accuracy.
  • Introduced a dynamic pruning gate for adaptive network structure control.
  • Implemented the theoretical model and developed an associated application.

Main Results:

  • The model demonstrated versatility, running effectively on both high-performance GPUs and low-power mobile platforms.
  • Achieved a high inference speed of 58 frames per second (FPS), surpassing mainstream models.
  • Enhanced detection accuracy for challenging subclasses using data augmentation and validated through ablation experiments.
  • Attained an overall model accuracy of 0.94.

Conclusions:

  • The dynamic-pruning method offers an efficient and accurate solution for plant disease detection.
  • The model's adaptability makes it suitable for a wide range of hardware, from powerful servers to mobile devices.
  • This research contributes a practical tool for agricultural monitoring and disease management.