Maize seedling/weed multiclass detection in visible/near infrared image based on SVM
基于SVM的可见/近红外光的玉米和杂草的多类识别

Weed detection play an important role in variables spraying in precision agriculture. This paper presents a new SVM (support vector machine) method using decision binary tree to discriminate crop and weeds in visible/near infrared image. Vegetation is segment from soil according to spectral feature in near-infrared band based on threshold method. The multi-spectral reflectance features of vegetation canopy are combined with texture features and shape features. Then multiclass detection is achieved based on decision binary tree established by maximum voting mechanism. It was tested by discriminate maize seedling and its associated weeds. The validation tests indicated that SVM using decision binary tree could improve classification accuracy significantly, and meet real-time requirements of agricultural applications greatly. The proposed method has produced results superior to other approaches.