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Position: Home > Articles > Weed Identification Method Based on Probabilistic Neural Network in the Corn Seedlings Field Journal of Agricultural Mechanization Research 2010,32 (11) 47-49+53

基于概率神经网络的玉米苗期杂草识别方法的研究

作  者:
侯晨伟;陈丽
单  位:
河北农业大学机电工程学院
关键词:
玉米幼苗;杂草识别;图像处理;概率神经网络
摘  要:
提出了一种基于计算机视觉技术和概率神经网络技术的玉米幼苗和杂草识别方法。首先利用类间方差最大自动阈值法对杂草图像的修正的超绿特征进行二值化处理;然后提取目标对象的形状特征参数作为输入向量,由概率神经网络(PNN)分类器识别出玉米幼苗。试验结果表明,该方法的有效性,对不同田间环境的玉米幼苗与杂草的准确识别率分别为92.5%和95%,效果优于使用BP网络。
译  名:
Weed Identification Method Based on Probabilistic Neural Network in the Corn Seedlings Field
作  者:
Hou Chenwei,Chen Li (Machine and Electric College,Hebei Agricultural University,Baoding 071001,China)
关键词:
corn seedling;weed identification;image processing;probabilistic neural network
摘  要:
This paper proposed a method of weed identification by using the technique of image processing and probabilistic neural network.Otsu's method for automatic threshold was applied to segment weeds images based on the modified excess green feature,it could distinguish the plant objects from the background effectively under various conditions.The probabilistic neural network classifier was created for recognition of corn seedlings and weeds according to the shape features.Comparing the probabilistic neural network (PNN) method with the back-propagation neural network one,the former is better than the latter seeing from the experimental results.The former method gave the recognition rate of 92.5% (corn seedlings) and 95% (weeds).

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