Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Aiming at the problems of noise, background interference and low detection in peach disease image, this paper proposes a detection method of peach disease based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.136421-136433
Main Authors: Huang, Shuangjie, Zhou, Guoxiong, He, Mingfang, Chen, Aibin, Zhang, Wenzhuo, Hu, Yahui
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Asymptotic methods
Asymptotic non-local means
Asymptotic properties
Background noise
Classification algorithms
Convolution
Diseases
extreme learning machine
Filtering
Interference
Machine learning
Mathematical model
Neural networks
Noise reduction
parallel convolution neural network
Particle swarm optimization
peach disease detection
Surface morphology
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at the problems of noise, background interference and low detection in peach disease image, this paper proposes a detection method of peach disease based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning machine(ELM) optimized by linear particle swarm optimization(IPSO). Firstly, the method uses the ANLM image denoising algorithm to reduce the interference of the complex background in the image, then uses the parallel convolution neural network proposed by this paper to identify the characteristics of peach disease, uses the improved elu activation function instead of the conventional ReLu activation function, and uses the linear particle swarm optimized ELM (IPELM) proposed by this paper in the last layer instead of the traditional softmax layer, the update method is improved from two aspects to improve the convergence speed and accuracy of the network effectively. The results of 25513 images showed that the highest detection accuracy of brown rot, black spot, anthracnose, scab and normal peach were 89.02, 90.56, 85.37, 86.70 and 89.91 percent respectively, which indicated that this method was an effective method for peach disease detection.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3011685