Research on feature extraction and recognition of common diseases of wheat leaf
Year: 2014
Type: article
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.
Author: Wang Meil
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W2349971721