Research on Feature Extraction of Wheat Leaf Disease Image

Zhi Hua Diao; Yuan Yuan Wu

doi:10.4028/www.scientific.net/AMR.317-319.1326

Paper Titles

Design of a Temperature Measurement System Using LabVIEW Virtual Instrument Modules
p.1308

Study of Alumina Concentration Control Based on Intelligent Characteristic Model
p.1314

The Summarization and Prospection of APF Control Means
p.1318

Development of a Novel Self-Adaptive Contacting Ultrasonic Inspecting System for Welding Belt on Pipeline
p.1322

Research on Feature Extraction of Wheat Leaf Disease Image
p.1326

Research of Corrosion Monitoring System for Metal Coatings
p.1330

Detection and Identification of Leakage Signal in the Explosive Gas Atmosphere
p.1337

Research on Five Sections Measuring Model in Process Pneumatic Measurement System for Inner Hole Honing
p.1342

Nonlinear System Fault Detection Based on TLLE
p.1347

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319Research on Feature Extraction of Wheat Leaf...

Research on Feature Extraction of Wheat Leaf Disease Image

Abstract:

In order to resolve the problem of not taking into account color, texture and shape features in crop disease intelligent recognition systems, feature extraction method based on three feature types was proposed. Two types of color spaces such as RGB and HIS were considered, and the transformation formula of the two color spaces was improved. Then ten color features were defined and extracted. Meanwhile sixteen texture features were defined and extracted based on gray level co-occurrence matrix. And thirteen shape features were defined and extracted based on invariant moment theory. Finally the feature dataset was received which was suitable for identifying four types of wheat leaf diseases. The results show that the system recognition rate is relatively high, and can meet the practical application requirements.