CAJ | 학술논문

利用计算机图像处理技术,依据植物叶片图像的形状特征对14种豆科牧草进行分类识别.通过对叶片图像进行预处理,提取出叶片的轮廓.在此基础上提取了叶片形状的全局特征和局部特征;全局特征包括叶片的横纵轴比、矩形度、圆形度等8项几何特征和7个图像不变矩特征;局部特征为叶缘粗糙度.利用PNN(Probabilistic neural network)和BPN(Back propagation network)作为分类器进行识别分类,实现了对豆科牧草叶片图像的分类.识别结果表明,PNN网络的平均识别率为85.1%、BPN网络的平均识别率为82.4%.
이용계산궤도상처리기술,의거식물협편도상적형상특정대14충두과목초진행분류식별.통과대협편도상진행예처리,제취출협편적륜곽.재차기출상제취료협편형상적전국특정화국부특정;전국특정포괄협편적횡종축비、구형도、원형도등8항궤하특정화7개도상불변구특정;국부특정위협연조조도.이용PNN(Probabilistic neural network)화BPN(Back propagation network)작위분류기진행식별분류,실현료대두과목초협편도상적분류.식별결과표명,PNN망락적평균식별솔위85.1%、BPN망락적평균식별솔위82.4%.
Traditionally, measure and species classification of plants are implemented by human experts, which is time-consuming and inefficient. In recent years, information technology including image processing and pattern recognition has been introduced into plant classification. Compared with flowers with 3D structures, leaves are easier to process by computer due to their 2D structures. This paper introduces a method of classifying plants of leguminous forage based on the leave shape features. Firstly, pre-processing method is used to extract the contour of a leaf. Then global and local features of the leaf shape are extracted. The global features include eight geometric features such as axis ratio, rectangularity, circularity, etc, and seven moment invariants. Roughness of leaf edge is selected as the local features. Finally, probabilistic neural network (PNN) and back propagation network (BPN) are applied to constructing classifiers. The experimental results show that the recognition rate of PNN and BPN is 85.1% and 82.4% respectively.