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A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Dengshan Li^1,2, Rujing Wang¹, Chengjun Xie¹

¹Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Sensors (Basel, Switzerland)

|January 25, 2020

Summary

This summary is machine-generated.

A new deep learning system effectively detects plant diseases and pests in videos, crucial for increasing grain production in food-scarce regions. This custom backbone architecture outperforms existing methods for untrained rice video detection.

Keywords:

deep convolutional neural network deep learning rice diseases and pests video detection video metrics

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Area of Science:

Agricultural Science
Computer Vision
Deep Learning

Background:

Increasing grain production is vital for food security in regions facing scarcity.
Timely control of crop diseases and pests is essential for boosting agricultural yields.

Purpose of the Study:

To develop a deep learning-based video detection architecture for identifying plant diseases and pests.
To establish a foundation for a real-time crop disease and pest video detection system.

Main Methods:

Video data was converted into still frames for analysis.
A Faster R-CNN framework was employed as the still-image detector.
A custom backbone was developed and integrated into the detection system.

Main Results:

The proposed system demonstrated superior performance in detecting diseases and pests in untrained rice videos compared to VGG16, ResNet-50, ResNet-101, and YOLOv3.
Image-training models were utilized for detecting blurry video segments.
Novel video-based evaluation metrics were introduced and validated.

Conclusions:

The custom backbone deep learning architecture is highly suitable for detecting plant diseases and pests in videos, particularly for untrained crop varieties.
The developed system offers a promising approach for enhancing crop monitoring and disease management strategies.