CAJ | 학술논문

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,实现了分类.对玉米幼苗及其伴生杂草的识别结果表明,基于SVM,利用决策二叉树的多类分类,可极大的提高分类精度,满足农业应用的实时性要求,与其他方法相比具有较好的结果.
잡초적식별분류재정준농업적변량분시중구유중요적작용.인차제출료일충신적기우SVM(지지향량궤),이용결책이차수재가견/근홍외도상중식별작물화잡초적방법.근거근홍외파단적광보특성,이용역치법실현료식물화토양배경적분할.장식물관층적다광보반사특정、문리특정화형상특정상결합,채용최대투표궤제산법구조합리적결책이차수,실현료분류.대옥미유묘급기반생잡초적식별결과표명,기우SVM,이용결책이차수적다류분류,가겁대적제고분류정도,만족농업응용적실시성요구,여기타방법상비구유교호적결과.
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.