Research on Color and Shape Recognition of Maize Diseases Based on HSV and OTSU Method - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

## Research on Color and Shape Recognition of Maize Diseases Based on HSV and OTSU Method

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1
Guifen Chen
• Function : Author
• PersonId : 972131
Ying Meng
• Function : Author
Jian Lu
• Function : Author
Dongxue Wang
• Function : Author

#### Abstract

With the application of IOT technology in maize disease images for monitoring and collecting, timely detection of the types and characteristics of identification of disease has become a hot research in the diagnosis and treatment of diseases and insect pests. In order to improve the recognition accuracy of maize leaf, achieve rapid diagnostic purposes, this paper takes the leaf spot of maize gray leaf spot and image as the research object, use the computer image processing technology is studied on the effective segmentation and recognition of color and shape features. The genetic algorithm was adopted to optimize the selection of maize disease images real-time filtering; $3 * 3$ mode noise suppression of the image selected by value smoothing; then select the HSV component of the color feature extraction of the disease; the maximum between class variance (OTSU) disease shape character segmentation and recognition. The results show that, based on genetic algorithm optimization based on image In HSV and Otsu method can be more accurate segmentation and recognition of the disease of color and shape features, and enhance the real-time and accuracy of the image of maize disease detection and recognition and oriented under the condition of things plant diseases and insect pests of maize and provide technical support.

#### Domains

Computer Science [cs]

### Dates and versions

hal-02179991 , version 1 (12-07-2019)

### Identifiers

• HAL Id : hal-02179991 , version 1
• DOI :

### Cite

Guifen Chen, Ying Meng, Jian Lu, Dongxue Wang. Research on Color and Shape Recognition of Maize Diseases Based on HSV and OTSU Method. 10th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2016, Dongying, China. pp.298-309, ⟨10.1007/978-3-030-06155-5_30⟩. ⟨hal-02179991⟩

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