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

Chen, Guifen; Meng, Ying; Lu, Jian; Wang, Dongxue

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-030-06155-5_30

Titre :

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

Auteur(s) :

Chen, Guifen [Auteur]
School of Information Technology
Meng, Ying [Auteur]
School of Information Technology
Lu, Jian [Auteur]
School of Information Technology
Wang, Dongxue [Auteur]
School of Information Technology

Éditeur(s) ou directeur(s) scientifique(s) :

Daoliang Li

Titre de la manifestation scientifique :

10th International Conference on Computer and Computing Technologies in Agriculture (CCTA)

Ville :

Dongying

Pays :

Chine

Date de début de la manifestation scientifique :

2016-10-19

Titre de l’ouvrage :

IFIP Advances in Information and Communication Technology

Titre de la revue :

Computer and Computing Technologies in Agriculture X

Éditeur :

Springer International Publishing

Date de publication :

2019

Mot(s)-clé(s) en anglais :

genetic algorithm
Internet of things
HSV
OTSU
maize diseases

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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