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Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax.

Ping Li¹, Rongzhi Jing¹, Xiaoli Shi¹

¹School of Electronic Information Engineering, Zhengzhou SIAS University, Zhengzhou, China.

Frontiers in Plant Science

|May 20, 2022

Summary

This study introduces a modified convolutional neural network (MCNN) for accurate apple disease recognition from leaf images. The MCNN method effectively identifies diseases, aiding in disease disaster assessment and management.

Keywords:

convolutional neural networks crop disease recognition disease module identification modified Softmax classifier modified convolutional neural networks

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Area of Science:

Computer Vision
Agricultural Science
Machine Learning

Background:

Accurate apple disease identification is crucial for effective prevention and treatment.
Disease disaster assessment relies heavily on timely and precise disease recognition.
Automated recognition of apple diseases from leaf images is an active research area.

Purpose of the Study:

To propose a novel apple disease recognition method using modified convolutional neural networks (MCNN).
To enhance recognition performance and model training speed.
To address challenges like gradient disappearance in deep learning models for image recognition.

Main Methods:

Developed a Modified Convolutional Neural Network (MCNN) incorporating the Inception module.
Utilized Global Average Pooling (GAP) to replace fully connected layers for faster training.
Implemented a modified Softmax classifier with a modified linear element activation function and local response normalization.

Main Results:

Experimental validation on two apple disease image datasets demonstrated the algorithm's feasibility.
The proposed MCNN achieved improved recognition performance compared to standard methods.
The modifications effectively mitigated the gradient disappearance problem.

Conclusions:

The developed MCNN offers a feasible and effective approach for automated apple disease recognition.
The method shows potential for practical application in agriculture for disease management.
Further research can explore broader applications and dataset expansions.