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Related Experiment Video

Updated: Jul 27, 2025

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Published on: March 17, 2023

An Accurate Classification of Rice Diseases Based on ICAI-V4.

Nanxin Zeng¹, Gufeng Gong¹, Guoxiong Zhou¹

¹College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China.

Plants (Basel, Switzerland)

|June 10, 2023

Summary

A new Candy algorithm and ICAI-V4 neural network improve rice disease classification by enhancing images and features. This method achieves 95.57% accuracy, aiding agricultural development.

Keywords:

Candy algorithm ICAI-V4 coordinate attention involution rice disease detection

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Published on: August 4, 2018

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Rice diseases like blast and blight significantly threaten crop yields.
Accurate rice disease classification is challenging due to image noise, blur, and similar disease appearances.

Purpose of the Study:

To develop an effective method for enhancing rice disease images and improving classification accuracy.
To introduce a novel image enhancement algorithm (Candy) and a new neural network (ICAI-V4) for robust rice disease identification.

Main Methods:

The Candy algorithm uses an improved Canny operator (gravitational edge detection) for noise reduction and edge feature emphasis.
A new neural network, ICAI-V4, based on the Inception-V4 backbone with coordinate attention and involution, was designed for enhanced feature extraction.
Leaky ReLU activation was employed to improve model robustness and prevent neuron death.

Main Results:

The ICAI-V4 model, combined with the Candy algorithm, demonstrated strong performance in classifying rice diseases.
Experiments using 10-fold cross-validation on 10,241 images yielded an average classification accuracy of 95.57%.

Conclusions:

The proposed Candy algorithm and ICAI-V4 neural network offer a feasible and high-performing solution for real-world rice disease classification.
This approach effectively addresses challenges posed by image quality and disease similarity, supporting agricultural monitoring and management.