Disease Recognition of Maize Leaf Based on KNN and Feature Extraction

Abstract

Maize is one of the main crops in Shangluo. The maize yield and quality are adversely affected by leaf spot and rust. In order to realize the early prevention of maize leaf spot and rust and avoid problems of environmental pollution induced by the conventional chemical reagents, computer vision technology was proposed for disease detection in this research. The algorithm of [Formula: see text]-means was used to process the image samples obtained from the test field. The healthy area, disease area and background area were separated. Based on the healthy area and disease area, ten parameters were extracted, including four parameters of color characteristic, four parameters of texture feature and two parameters of shape feature, which were taken as the classification criteria in the classification training by KNN algorithm. In total, 200 test image samples (100 samples of leaf spot and 100 samples of maize rust) were sent into the training model for disease identification. The results showed that the algorithm proposed can efficiently and nondestructively identify maize leaf spot and rust based on image segmentation and multi-feature fusion. The method was convenient and environmentally friendly. It can provide technical support for plant protection and can supply research ideas of precise control of crop diseases.