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Construction and verification of machine vision algorithm model based on apple leaf disease images.

Gao Ang¹, Ren Han¹, Song Yuepeng^1,2,3

¹College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, Shandong, China.

Frontiers in Plant Science

|October 2, 2023

Summary

This study introduces LALNet, a fast machine vision model for detecting apple leaf diseases. The high-speed apple leaf network achieves 96.07% accuracy, enabling precision spraying and improving fruit yield.

Keywords:

apple leaf disease deep learning deep separable convolution leaf detection network re-parameterization

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Apple leaf diseases significantly impact fruit quality and yield, necessitating timely and accurate detection methods.
Intelligent detection systems are crucial for effective disease management in apple orchards.

Purpose of the Study:

To propose a novel machine vision algorithm model, LALNet (High-speed apple leaf network), for rapid and accurate detection of apple leaf diseases.
To develop an efficient deep learning architecture suitable for deployment on embedded devices for real-time disease monitoring.

Main Methods:

Designed an efficient apple leaf detection stacking module (EALD) using multi-branch and depth-separable convolutions.
Employed a backbone network with four superimposed EALD layers and an SE module for enhanced feature attention.
Utilized structural reparameterization to optimize operational speed during inference.

Main Results:

LALNet achieved a detection accuracy of 96.07% on the test set.
The model demonstrated high performance with total precision of 95.79%, recall of 96.05%, and F1 score of 96.06%.
The model boasts a small size (6.61 MB) and a fast detection speed of 6.68 ms per image.

Conclusions:

LALNet offers a compelling balance of high detection accuracy and rapid execution speed, making it ideal for embedded applications.
The developed model supports precision spraying strategies for efficient apple leaf disease prevention and control.
This technology can contribute to improved orchard management and increased crop yields.