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| 基于太赫兹成像技术的大豆叶片水分含量测定 | |
| 引用本文： | 步正延,李臻峰,宋飞虎,李斌,李静.基于太赫兹成像技术的大豆叶片水分含量测定[J].浙江农业学报,2018,30(8):1420. |
| 作者姓名： | 步正延 李臻峰 宋飞虎 李斌 李静 |
| 作者单位： | 1.江南大学 机械工程学院,江苏 无锡 214000; 2.江苏省食品先进制造装备技术重点实验室,江苏 无锡 214122; 3.国家农业信息化工程技术研究中心,北京 100097 |
| 基金项目： | 国家自然科学基金(51406068); 江苏省政策引导类计划(产学研合作)——前瞻性联合研究(BY2015019-16); 江苏省食品先进制造装备技术重点实验室开放基金(FM-201504) |
| 摘 要： | 为了实现叶片水分含量的快速、精准检测,提出一种基于太赫兹成像技术的大豆叶片水分含量测定方法。利用太赫兹光谱成像系统获取96份大豆叶片太赫兹图像,采用干燥法测量叶片含水率,通过主成分分析(PCA)提取出水分敏感特征波段0.557、1.098、1.163 THz,对这3个特征波段下的叶片图像采用自适应阈值分割法,将其分为叶脉图像与叶肉图像,分别求取各自的图像灰度特征,并分为叶片特征组(G1)、叶脉特征组(G2)和叶肉特征组(G3)。分别采用多元线性回归(MLR)、反向传播神经网络(BP-ANN)和最小二乘支持向量机(LS-SVM)算法,以上述3个特征组作为输入,构建出9种大豆叶片水分预测模型。对比分析各模型性能,发现基于G3的LS-SVM模型预测结果最好,校正集和预测集的决定系数和均方根误差分别为0.967 8、0.963 2,0.057 8、0.046 5。试验结果表明,利用太赫兹成像技术来检测叶片中的水分含量具有非常高的预测精度,为作物叶片水分含量测定提供了一种行之有效的检测手段。 |
| 关 键 词： | 太赫兹 叶片 含水率 主成分分析 图像处理 数据建模 |
| 收稿时间： | 2017-11-09 |
Determination of moisture content in soybean leaves based on terahertz imaging |
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| BU Zhengyan,LI Zhenfeng,SONG Feihu,LI Bin,LI Jing.Determination of moisture content in soybean leaves based on terahertz imaging[J].Acta Agriculturae Zhejiangensis,2018,30(8):1420. | |
| Authors: | BU Zhengyan LI Zhenfeng SONG Feihu LI Bin LI Jing |
| Institution: | 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214000, China; 2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China; 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China |
| Abstract: | In order to obtain water information from plant leaves rapidly and accurately, a new method for determining moisture content in soybean leaves was developed on the basis of the terahertz spectral imaging system. The terahertz images of selected 96 soybean leaves with different moisture contents were captured by the terahertz time-domain spectroscopy system (THz-TDS), and the moisture contents of leaves were measured by the electronic scale. Principal component analysis (PCA) was conducted on the 0.2-1.6 THz terahertz images, and three effective bands 0.557, 1.098, 1.163 THz were determined. Adaptive threshold segmentation was adopted to divide the leaf image into the vein image and the mesophyll image. Then gray features of the gained images of leaf, vein and mesophyll were computed,which were classified into three groups: the group of leaf (G1), the group of vein (G2) and the group of mesophyll (G3). At the same time, algorithms of multiple linear regression (MLR), back propagation (BP) neural network (BP-ANN) and least squares support vector machine (LS-SVM) were used to establish 9 prediction models of moisture content in soybean leaves, and the 3 groups were used as input. It turned out that the LS-SVM model based on G3 had the best prediction results among all models, as the determination coefficient of the calibration set and the prediction set reached 0.967 8 and 0.963 2, respectively, and the root mean square errors were 0.057 8 and 0.046 5, respectively. The experiment results showed that the proposed method was accurate and offered an effective means to measure the moisture content of crop leaves. |
| Keywords: | terahertz leaves moisture content principal component analysis image processing data modeling |
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