基于LabVIEW葡萄叶片还原糖含量的测试方法

Test Method for Reducing Sugar Content of Grape Leaves Based on LabVIEW

目前对葡萄叶片营养元素诊断大都利用化学滴定法或者光谱仪器分析法,但此类方法仅适用于实验室小批量作物测试,难以应用至大批量农业生产中。因此本系统以葡萄叶片为测试对象,在LabVIEW软件平台下,提出了一种基于夜间葡萄叶片中还原糖含量的测定系统。具体为利用USB工业摄像头实时采集叶片图像,借助于Vision Development Module模块中的Vision Assistant完成图像处理工作,进而通过LabVIEW中"脚本与公式"模块调用Matlab Script脚本节点编程,提取叶片图像颜色和纹理特征值参数,使用支持向量机(SVM)算法构建分类器模型对487幅葡萄叶片糖分含量进行分类识别。结果表明该系统分类识别准确率高达87.349%,单次测试时长为2~5 min。证明该系统精度高、工作稳定,对提高农业经济效益方面具有重要意义,有实用价值。

At present,chemical titration or spectral analysis are mostly used for the diagnosis of nutritional elements of grape leaves,but such methods are only suitable for laboratory small batch crop testing,it is also difficult to realize in large-scale agricultural production.Therefore,grape leaves are took as the test object,on the platform of LabVIEW software,a determination system of reducing sugar content in grape leaves at night was proposed.The leafs image was captured using the USB industrial camera in real time,the Vision Assistant in Vision Development Module was used to complete the image processing,and then Matlab Script node programming is called by the"Script and Formula"module in LabVIEW to extract the color and texture characteristic parameters of leaf images,support vector machine(SVM)algorithm was used to construct a classification model to classify and identify the sugar content of 487 grape leaves.The results show that the correct rate of the system is as high as 87.349%,and the single test time is 2 to 5 minutes.It is proved that the system has high precision and stable work,which is of great significance to improve agricultural economic benefits and has practical value.