基于叶片图像算法的植物种类识别方法研究

Plant species recognition based on leaf image algorithm

为了提高植物种类的识别率,采用叶片图像算法。首先建立植物种类特征模型,包括植物叶片颜色特征、形状特征、纹理特征;然后确定径向基函数神经网络的输入层、输出层、隐含层之间的关系;接着对径向基函数个数、中心及宽度优化,基于梯度下降方法对权重参数计算,自适应调节学习率;最后给出了植物种类识别过程。实验仿真选择植物叶片颜色特征、形状特征、纹理特征的特征量分别为6、7、7个,其中本文算法对植物种类识别的三个组合特征平均识别率为93.5%,高于单个特征、两个组合特征的平均识别率,形状特征对识别率所起的作用最大。

In order to improve the recognition rate of plant species,leaf image algorithm was proposed. Firstly,the plant species characteristic model was established,including leaf color,shape and texture characteristics. Secondly,relations of input,output and hidden layer of radial basis function neural network was built. Thirdly,the numbers of radial function,center and width was optimized,weighting parameter was calculated based on gradient descent method,and learning rate was adaptively adjusted. Finally,plant species recognition process was given. Plant leaf color,shape and texture characteristic numbers of simulation were selected as 6,7 and 7,the three characteristics average recognition rate of improved radial basis function neural network algorithm was 93. 5% ,higher than single and two characteristics,the shape characteristic had maximum recognition rate.