|Table of Contents|

Early Detection of Powdery Mildew Infected Rose Leaves Based on Thermal Image Characteristics(PDF)

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2019年14
Page:
145-150
Research Field:
Publishing date:

Info

Title:
Early Detection of Powdery Mildew Infected Rose Leaves Based on Thermal Image Characteristics
Author(s):
ZHANG GuoqiTIAN Yanting
(1.School of Physics and Optoelectronic Engineering,Taiyuan University of Technology,Taiyuan,Shanxi 030600;2.School of Physics and Optoelectronic Engineering,Taiyuan University of Technology,Taiyuan,Shanxi 030600)
Keywords:
neuro-fuzzy classificationk-means clusteringhot histogram
PACS:
-
DOI:
10.11937/bfyy.20183100
Abstract:
The surface temperature of infected and uninfected rose plants was observed by digital infrared thermal imager.The leaf temperature of the infected area was increased by 2.3 ℃.In addition,by classifying healthy and infected leaves,select the best experimental leaves and observe their thermal characteristics;use absolute temperature measurement to select their temperature maximum,minimum,median,maximum temperature difference,standard deviation,and fit the data.To the standard normal distribution and the Laplacian distribution curve,then the neural fuzzy classifier was used to identify the infected and healthy leaves;finally,the k-means clustering method was used to obtain the original parameters and fuzzy rules,and the 8 clusters of the classifier were trained.And testing,the accuracy rate reached 92.55% and 92.30%.The results showed that drought had an adverse effect on healthy leaves.Under drought conditions,the positive predictive value and specificity index of healthy leaves decreased correspondingly,while the performance of leaves did not significantly affect in the dark.

References:

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Last Update: 2019-08-08