基于SVM的可见/近红外光的玉米和杂草的多类识别

杂草的识别分类在精准农业的变量喷施中具有重要的作用.因此提出了一种新的基于SVM(支持向量机),利用决策二叉树在可见/近红外图像中识别作物和杂草的方法.根据近红外波段的光谱特性,利用阈值法实现了植物和土壤背景的分割.将植物冠层的多光谱反射特征、纹理特征和形状特征相结合,采用最大投票机制算法构造合理的决策二叉树,实现了分...

Maize seedling/weed multiclass detection in visible/near infrared image based on SVM

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.