基于计算机视觉的土壤镉胁迫生菜叶片污染响应分析

摘要

In order to achieve nondestructive detection of heavy metal cadmium in lettuce leaves,computer vision technology was used as the research method,which combined image processing method and feature selection method,to identify four gradients of heavy metal cadmium stress lettuce leaves.First of all,the leaf image of lettuce was obtained by digital camera.Then,the K-means clustering algorithm was used to segment the image,and the color,shape and texture of the image were extracted from the extracted target image.A total of 46 image features were obtained.In order to make the model easier and reduce the amount of data,the image feature was dimensioned by competitive adaptive reweighted sampling (CARS) and variable importance analysis based on random variable combination (VIAVC).The partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to construct the model for identification of cadmium stress in lettuce.The results showed that in the seven combined feature models,the optimal model was given by the model of color,shape and texture fusion.The accuracy of the training set classification was 92％.The color,shape and texture fusion features were reduced by CARS and VIAVC,and it was found that the dimensionality and visualization of VIAVC were better than those of CARS.Using the reduced dimension of the low-dimensional mapping point to build the model,the accuracy of the training set classification and accuracy of the prediction set of RF model were higher than those of the PLS-DA.Among them,the accuracy of the training set and predictive set classification based on VIAVC dimensionality reduction were 98.0％ and 96.0％,respectively.It can be seen that the RF model based on VIAVC dimensionality can better identify the lettuce leaves with different cadmium stress levels under the premise of greatly reducing the feature dimension.%为了实现无损检测生菜叶片中重金属镉的污染程度,以计算机视觉技术为研究手段,结合图像处理方法和特征选择方法,对4个梯度重金属镉胁迫的生菜叶片进行识别.首先利用数码相机获取生菜叶片图像,然后使用K-means聚类算法分割图像,对分割出的目标图像提取图像颜色、形状和纹理特征,共获取46个图像特征.为了使模型更简便和减少数据量,利用基于变量组合的变量重要性分析(VIAVC)和竞争性自适应重加权算法(CARS)对图像特征进行降维.采用偏最小二乘法判别分析(PLS-DA)和随机森林(RF)构建模型,用于生菜镉胁迫程度的识别.结果表明,在7个组合特征模型中,颜色形状纹理融合特征所建立的模型给出了最优结果,测试集分类正确率为92％.用VIAVC和CARS对颜色形状纹理融合特征进行特征选择,发现VIAVC的降维效果优于CARS.使用特征选择的变量建立模型,RF模型的训练集分类正确率和预测集分类正确率均高于PLS-DA,其中,基于VIAVC的RF模型的训练集和预测集分类正确率分别为98.0％和96.0％.可见,基于VIAVC的RF模型在大大降低了特征维数的前提下,能够较好地对不同镉胁迫程度的生菜叶片进行识别.