| Research on methods of diagnosing crop water-deficiency based on image processing |
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| DOI：10.7606/j.issn.1000-7601.2013.01.18 |
| Key Words: corn leaf water deficiency diagnosing image processing colour feature extr
action linear regression |
| Author Name | Affiliation | | XU Tengfei | College of Mechanical and Electric Engineering, Northwest A&F University, Y
angling, Shaanxi 712100, China | | HAN Wenting | Institute of Water Saving Agriculture in Area Regions of China, Northwest A&F University, Yangling, Shaanxi 712100, China
Institute of Soil and Water Conservation, China Academy of Sciences, Yangling，Shaanxi 712100, China | | SUN Yu | College of Mechanical and Electric Engineering, Northwest A&F University, Y
angling, Shaanxi 712100, China |
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| Abstract: |
| Digital image processing techniques were used to evaluate crop water stress, by
cultivating 90 plants of corn with different irrigation amounts in a greenhouse.
The Canon IXUS110 digital camera with 12.1 million pixels was used to capture
the images of corn leaves after being picked from the plants during the heading
stage, and then the moisture content of the leaves was detected by using drying
method. The eigenvalues of mean, kurtosis, variance, skew degree, energy and ent
ropy were calculated by using grey histogram of leaf images. The data extracted
from the leaves of 20 samples were used to set up the linear regression model sh
owing the relationship between the mean and the leaf moisture, and the other 20
samples were used to verify the model. The standard deviation of the validation
results was 0.021. It was concluded that of the eigenvalue of mean of leaf image
s could be used to predict the moisture content in corn leaves. |
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