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Updated: May 15, 2025

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Identification of rice leaf disease based on DepMulti-Net.

Kui Hu^1,2, Xinying Zheng^2,3, Xinyao Su⁴

¹School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, Hunan, China.

Frontiers in Plant Science

|April 11, 2025

Summary

A new rice disease identification model, DepMulti-Net, efficiently detects common diseases with 98.56% accuracy. This lightweight solution aids smart agriculture by overcoming complex background challenges in rice leaf disease identification.

Keywords:

DepMulti-Net convolutional Neural Network depthseparable convolution feature reuse multi-scale feature fusion rice leaf diseases

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Rice leaf disease identification faces challenges like complex backgrounds and feature extraction.
Existing models often have large parameter volumes, limiting efficiency.

Purpose of the Study:

To develop a novel, efficient, and lightweight model for rice leaf disease identification.
To address challenges in feature extraction and complex backgrounds in disease detection.

Main Methods:

Constructed a dataset of 20,000 rice disease images covering four common diseases.
Introduced a VGG-block module with depth-separable convolution to reduce parameters.
Designed a multi-scale feature fusion module and integrated feature reuse with an inverse bottleneck structure.

Main Results:

DepMulti-Net achieved 98.56% average accuracy in identifying four major rice diseases.
The model has a significantly reduced parameter count of only 13.50M.
Outperformed existing rice leaf disease identification methods in experimental evaluations.

Conclusions:

DepMulti-Net provides an efficient and lightweight solution for crop disease identification.
The model's design effectively handles complex backgrounds and enhances fine-grained feature recognition.
This research contributes to the advancement of smart agriculture through improved disease detection technology.