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Automating Citrus Budwood Processing for Downstream Pathogen Detection Through Instrument Engineering

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Published on: April 21, 2023

Deep Learning Based Automatic Grape Downy Mildew Detection.

Zhao Zhang^1,2,3,4, Yongliang Qiao⁵, Yangyang Guo^1,3,4

¹College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China.

Frontiers in Plant Science

|June 27, 2022

Summary

This summary is machine-generated.

This study introduces YOLOv5-CA, a deep learning model for detecting grape downy mildew (GDM). It offers accurate and fast disease identification, crucial for precision viticulture and protecting grape yields.

Keywords:

attention mechanism data augmentation deep learning digital agriculture disease detection grape downy mildew

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Published on: December 22, 2017

Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Grape downy mildew (GDM) significantly reduces grape yield and quality.
Manual GDM detection is labor-intensive and time-consuming.
Automated detection using AI and computer vision is needed for precision viticulture.

Purpose of the Study:

To develop an accurate and fast deep learning model for GDM detection.
To enhance GDM detection performance by integrating a coordinate attention (CA) mechanism into YOLOv5.
To evaluate the model's effectiveness in natural vineyard environments.

Main Methods:

A deep learning approach, YOLOv5 with a coordinate attention (CA) mechanism (YOLOv5-CA), was proposed.
A dataset of GDM was collected under diverse natural vineyard conditions.
The YOLOv5-CA model was trained and evaluated against other popular methods.

Main Results:

YOLOv5-CA achieved high detection accuracy: 85.59% precision, 83.70% recall, and 89.55% mAP@0.5.
The model demonstrated superior performance compared to Faster R-CNN, YOLOv3, and YOLOv5.
Inference speed reached 58.82 frames per second, enabling real-time application.

Conclusions:

YOLOv5-CA provides a robust and efficient solution for automated grape downy mildew detection.
The integration of the CA mechanism effectively enhances the model's ability to identify disease-related features.
This approach supports rapid and accurate field diagnosis for disease management in viticulture.