Abstract
This paper selects four common diseases of wheat leaf images, using image processing techniques to identify different types of disease. Firstly, the RGB color space is converted to HSV color space, the relevant color characteristics(hue and saturation)are extracted, and then geometry features(perimeter area, squareness, roundness, eccentricity, etc.) are extracted. To obtain the eigenvalues of each disease range, the sample images are analyzed, and then the eigenvalues of the unknown samples are used to identify different kinds of wheat diseases. This research takes powdery mildew and rust(leaf rust, stripe rust and stem rust)as research objects. Based on color characteristics, the powdery mildew and rust are identified, according to the shape characteristics, leaf rust, stripe rust and stem rust are identified. The proposed method is simple and convenient with an identification rate of more than 96%. The experimental results show that the chosen color and shape features of these four common diseases are valid and feasible for wheat diseases identification.
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Claims 3
Claim · model-written summary
Color features distinguish powdery mildew from rust in wheat images.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- powdery mildew and rust are identifiede0
- Comparator
- powdery mildew and ruste0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Based on color characteristics, the powdery mildew and rust are identified”
t0“wheat leaf”
Claim · model-written summary
Shape features distinguish leaf rust, stripe rust and stem rust in wheat images.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- leaf rust, stripe rust and stem rust are identifiede0
- Comparator
- leaf rust, stripe rust and stem ruste0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“according to the shape characteristics, leaf rust, stripe rust and stem rust are identified”
t0“wheat leaf”
Claim · model-written summary
The wheat disease identification method achieved an identification rate above 96%.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- identification ratee0
- Value
- more than 96%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The proposed method is simple and convenient with an identification rate of more than 96%.”
t0“wheat leaf”