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WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method.

Haiyun Liu^1,2, Lin Jiao^1,3, Rujing Wang^1,2,4

¹Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei, China.

Frontiers in Plant Science

|June 10, 2022

Summary

This summary is machine-generated.

Accurate detection of wheat stripe rust is crucial for crop yield. A new WSRD-Net model improves detection of this disease, even with its challenging orientation and large size, boosting agricultural productivity.

Keywords:

arbitrary-oriented convolutional neural network deep learning detection wheat strip rust

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Area of Science:

Plant Pathology
Computer Vision
Agricultural Science

Background:

Wheat stripe rust significantly reduces crop yield and causes economic losses.
Existing detection methods struggle with the arbitrary orientation and large aspect ratio of wheat stripe rust.

Purpose of the Study:

To develop an accurate and robust method for detecting wheat stripe rust.
To address the limitations of current convolutional neural network (CNN) based detection models.

Main Methods:

Introduced WSRD-Net, a refined single-stage rotation detector based on RetinaNet.
Incorporated a feature refinement module (FRM) to handle large aspect ratios and feature misalignment.
Created an oriented annotation dataset (WSRD2021) of in-field wheat stripe rust images.

Main Results:

WSRD-Net achieved 60.8% AP and 73.8% Recall on the WSRD2021 dataset, outperforming other oriented object detection models.
WSRD-Net demonstrated superior localization accuracy compared to horizontal object detection models for diseased areas.

Conclusions:

WSRD-Net effectively detects wheat stripe rust, addressing challenges posed by its orientation and aspect ratio.
The developed method offers a significant advancement for disease detection in agriculture, improving crop quality and economic outcomes.