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A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks.

Xiaoyue Xie1, Yuan Ma1, Bin Liu1,2,3

  • 1College of Information Engineering, Northwest A&F University, Yangling, China.

Frontiers in Plant Science
|June 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Faster DR-IACNN, a deep learning model for real-time detection of grape leaf diseases like black rot and mites. The model achieves 81.1% mAP, offering a feasible solution for disease diagnosis and plant health management.

Keywords:
convolutional neural networksdeep learningfeature fusiongrape leaf diseasesobject detection

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Grape cultivation is significantly impacted by diseases such as black rot, black measles, leaf blight, and mites.
  • Current methods for detecting grape leaf diseases lack real-time capabilities, hindering timely intervention and crop management.

Purpose of the Study:

  • To develop a real-time detection system for common grape leaf diseases.
  • To improve the accuracy and efficiency of grape disease diagnosis using deep learning.

Main Methods:

  • A grape leaf disease dataset (GLDD) was created using digital image processing.
  • An improved deep convolutional neural network, Faster DR-IACNN, was developed by integrating Inception-v1, Inception-ResNet-v2 modules, and SE-blocks into the Faster R-CNN algorithm.
  • The model was trained and evaluated on the constructed GLDD.

Main Results:

  • The Faster DR-IACNN model achieved a mean average precision (mAP) of 81.1% on the GLDD.
  • The detection speed of the model reached 15.01 frames per second (FPS).

Conclusions:

  • The Faster DR-IACNN model offers a feasible and efficient deep learning-based solution for the real-time diagnosis of grape leaf diseases.
  • This research provides valuable insights for developing similar detection systems for other plant diseases.