Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration

Leaf wetness duration (LWD) is a critical parameter used to predict plant disease, but its determination under actual field conditions is a major challenge. In this study, a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar greenh...

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Bibliographic Details
Published inInternational journal of agricultural and biological engineering Vol. 13; no. 3; pp. 161 - 167
Main Authors Wen, Dongmei, Ren, Aixin, Ji, Tao, Maria Flores-Parra, Isabel, Yang, Xinting, Li, Ming
Format Journal Article
LanguageEnglish
Published Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.05.2020
Subjects
Algorithms
Cameras
Cluster analysis
Clustering
Cucumbers
Estimates
Greenhouses
Humidity
Image analysis
Image processing
Image segmentation
Infrared cameras
Infrared imagery
Infrared imaging
Leaves
Moisture content
Pathogens
Plant diseases
Plants
Scanning
Sensors
Statistical analysis
Studies
Thermal imaging
Vector quantization
Vision systems
Visual observation
Beijing China
United States > US
China
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Summary:Leaf wetness duration (LWD) is a critical parameter used to predict plant disease, but its determination under actual field conditions is a major challenge. In this study, a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar greenhouse. Thermal images of the plant leaves were captured using an infrared scanning camera, and a leaf wetness area segmentation method consisting of two procedures was applied. First, a color space conversion was performed automatically by an image-processing algorithm. Then, the ř-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal image. Subsequently, to enable overall thermal image analysis, an initial leaf wetness threshold (LWT) of 5% was defined (where wetness values higher than 5% indicated that the leaf was in a wet state). The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values, because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning camera. While these differences were not found to be statistically significant in this study, the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses.
ISSN:1934-6344
1934-6352
DOI:10.25165/j.ijabe.20201303.4301