Maize Disease Recognition via Fuzzy Least Square Support Vector Machine
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@Article{JICS-8-316,
author = {Cunlou Lu, Shangbing Gao and Zecheng Zhou},
title = {Maize Disease Recognition via Fuzzy Least Square Support Vector Machine},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {8},
number = {4},
pages = {316--320},
abstract = { In this paper, we propose a new approach to recognize the maize disease, which is based on
fuzzy least square vector machine (FLSVM) algorithm. According to the texture characteristics of Maize
diseases, it uses YCbCr color space technology to segment disease spot, and uses the co-occurrence matrix
spatial gray level layer to extract disease spot texture feature, and uses FLSVM to class the maize disease. In
this method, the sample mean is calculated, and the center of each class is got; then the distance between the
sample and the center is calculated, according to the distance sample’s initial membership is got; by finding
K neighbors for each sample point, the sample membership degree is calculated according to the fuzzy K
nearest neighbor method. Extensive experiments on public datasets show that the algorithm can effectively
identify the disease image, the accuracy was as high as 98% or more.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22608.html}
}
TY - JOUR
T1 - Maize Disease Recognition via Fuzzy Least Square Support Vector Machine
AU - Cunlou Lu, Shangbing Gao and Zecheng Zhou
JO - Journal of Information and Computing Science
VL - 4
SP - 316
EP - 320
PY - 2024
DA - 2024/01
SN - 8
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22608.html
KW - Maize disease
KW - Support vector machine
KW - Image processing
KW - traffic sign
AB - In this paper, we propose a new approach to recognize the maize disease, which is based on
fuzzy least square vector machine (FLSVM) algorithm. According to the texture characteristics of Maize
diseases, it uses YCbCr color space technology to segment disease spot, and uses the co-occurrence matrix
spatial gray level layer to extract disease spot texture feature, and uses FLSVM to class the maize disease. In
this method, the sample mean is calculated, and the center of each class is got; then the distance between the
sample and the center is calculated, according to the distance sample’s initial membership is got; by finding
K neighbors for each sample point, the sample membership degree is calculated according to the fuzzy K
nearest neighbor method. Extensive experiments on public datasets show that the algorithm can effectively
identify the disease image, the accuracy was as high as 98% or more.
Cunlou Lu, Shangbing Gao and Zecheng Zhou. (2024). Maize Disease Recognition via Fuzzy Least Square Support Vector Machine.
Journal of Information and Computing Science. 8 (4).
316-320.
doi:
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