| 王松磊,吴龙国,王彩霞,何建国.可见近红外高光谱快速诊断番茄叶片含水量及其分布[J].光电子激光,2019,30(9):941~950 |
| 可见近红外高光谱快速诊断番茄叶片含水量及其分布 |
| Vis-NIR hyperspectral imaging for rapid diagnose and distribution of water cont ent in tomato leaves |
| 投稿时间：2019-03-18 |
| DOI： |
| 中文关键词: 高光谱成像技术 叶片水分含量 光谱响应 可视化 |
| 英文关键词:hyperspectral imaging water contents of leaves spectral response visualiza tion |
| 基金项目:宁夏回族自治区基金项目(2019AAC03057)和国家自然科学基金项目(31660484)资助项目 (1．宁夏大学 土木与水利工程学院,宁夏 银川 750021; 2．宁夏大学 农学院,宁夏 银川 750021) |
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| 中文摘要: |
| 基于可见近红外高光谱建立番茄叶片水分含量快 速诊断模型,对不同光谱处理及建模进行优选, 对水分含量分布进行可视化研究。结合阈值法采集不同生长期192个 番茄叶片感兴趣区域光谱信息进行预 处理比较,分析β权重系数法、连续投影算法(SPA)、无信息 变量消除(UVE)、竞争自适应加权算法(CARS) 及UVE-SPA、CARS-SPA组合方法特征波长优化方法,利用提取特征波长对多元线性回归(MLR)、主成分回 归(PCR)及偏最小二乘回归(PLSR)水分含量建模方法进行有效性评价,优化出最佳组 合模型,采用特征 图像光谱反射权重系数实现叶片含水量及其分布的可视化,解析叶片含水量光谱响应特性。 最终确立 Baseline为最佳波段预处理方法,全波段建模预测集相关系数Rp 达0.97；提取特征波长后,Baseline-CARS-MLR 为叶片水分含量预测最佳模型,预测集相关系数Rp为 0. 95,预测集均方根误差RMSEP为0.042。基于高光 谱成像技术快速评估叶片水 分含量具有一定优势,为活体番茄植株生长水分亏缺状况实时评估及智能化灌 溉技术提供理论依据。 |
| 英文摘要: |
| Based on the Visible and near infrared (Vis-NIR) hyperspectral imaging system to establish a rapid diagnosis and prediction models of water content in tomato leaves,the best proc essing and modeling methods for spectral data were chosen,and then the spectral response features of leaves mois ture content to different bands was discussed,Ultimately chemical imaging and visualization analysis were carried ou t on the distribution of water content in the leaves according to prediction models.Combined with thresholding approach the spectrum of region of interest (ROI) which gained from 110tomato leaves samples with differ ent growth periods were acquired,then a variety of pretreatment methods were used to optimize the(Mi croscopic hyperspectral imaging detection and mechanism of spectral respons e of peroxidase in tomato cell) spectrum,and the methods of β weight coefficient,Succe ssive Projections Algorithm(SPA),Uninformative Variable Elimination(UVE),Competitive Adaptive Weighting Algorithm(CARS) and the combination of UVE-SPA and CARS-SPA were analyzed to extract the feature wavelength from the optimized spectral data,the validity of modeling with Multiple Linear Regression(MLR),Principal Components Regression(PCR) and Partial Least Squares Regression (PLSR) for moisture content prediction were evaluated b y extracting wavelength,subsequently the optimal combined model was obtained,and the visuali zation of leaf water content and its distribution was realized with weight coefficient assignment method,at last the spectral response characteristics for water content in different bands were parsed.Conclusively,th e model of Baseline-CARS-MLR achieved the highest correlation coefficient (Rp) of 0.95for predicting with RMSEP of 0.042.It has certain advantage to quickly assess the moisture content of leaves based on high spectra l imaging,all the researches were to provide a theoretical basis for real-time evaluation of the water deficit of tomato plants and the development of intelligent irrigation technology. |
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