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Automatic Diagnosis of Rice Diseases Using Deep Learning.

Ruoling Deng¹, Ming Tao¹, Hang Xing¹

¹College of Engineering, South China Agricultural University, Guangzhou, China.

Frontiers in Plant Science

|September 7, 2021

Summary

This study introduces a smartphone app for accurate rice disease diagnosis using deep learning. The app achieves 91% accuracy in identifying six common rice diseases, improving crop yield protection.

Keywords:

convolutional neural network deep learning diagnosis ensemble learning rice disease

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Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Rice diseases significantly reduce crop yield.
Current diagnostic methods lack accuracy and efficiency, often requiring specialized equipment.
Accurate and timely disease diagnosis is crucial for effective crop management.

Purpose of the Study:

To develop an automated, efficient, and accurate rice disease diagnosis system.
To implement the system in a user-friendly smartphone application for field use.
To leverage deep learning for improved rice disease identification.

Main Methods:

A large dataset of 33,026 images covering six rice diseases was compiled.
Deep learning techniques, specifically an Ensemble Model integrating DenseNet-121, SE-ResNet-50, and ResNeSt-50 submodels, were employed.
The Ensemble Model was trained and validated on separate image sets.

Main Results:

The Ensemble Model achieved an overall accuracy of 91% in diagnosing six rice diseases.
The model demonstrated reduced confusion and misdiagnosis among visually similar diseases.
Selected submodels (DenseNet-121, SE-ResNet-50, ResNeSt-50) showed superior performance attributes.

Conclusions:

The developed smartphone app provides a convenient and efficient tool for field-based rice disease diagnosis.
The deep learning-based Ensemble Model offers a significant improvement over existing methods.
This technology aids in protecting crop yield by enabling prompt and accurate identification of rice diseases like leaf blast, false smut, and sheath blight.