基于高光谱图像的黄瓜叶片叶绿素含量分布检测

邹小波; 张小磊; 石吉勇; 李志华; 申婷婷

doi:10.3969/j.issn.1002-6819.2014.13.021

基于高光谱图像的黄瓜叶片叶绿素含量分布检测

江苏大学食品与生物工程学院，镇江 212013

基金项目: 国家863项目（2011AA100807），江苏省杰出青年基金（BK20130010），新世纪优秀人才项目（NCET-11-0986），江苏特聘教授、全国优秀博士基金（200968），国家自然科学基金（ 61301239），江苏省自然科学基金（BK20130505）

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- 收稿日期: 2013-11-20
- 修回日期: 2014-05-30
- 发布日期: 2014-06-30

Detection of chlorophyll content distribution in cucumber leaves based on hyperspectral imaging

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China

摘要

摘要: 植物叶片叶绿素含量及分布是植物营养信息表达的重要指标。为了给大棚黄瓜营养元素的控制提供理论依据，该研究利用高光谱图像建立简单实用的光谱值和叶绿素含量关系的模型，从而实时、无损地检测叶片的叶绿素分布。选取黄瓜叶片的高光谱图像数据块中450～850 nm波段作为研究波段。选取8个具有代表性的植被指数，建立特征波长λ下相应的光谱反射值Rλ与黄瓜叶片叶绿素含量之间的关系模型。结果显示，基于最优指数(R695-705)?1?(R750-800)?1的模型可以很好地预测黄瓜叶片叶绿素的含量，校正集和预测集相关系数r分别为0.8410和0.8286，最小均方根误差RMSE分别为0.2045和0.2190 mg/g。最后根据最优模型预测叶片上任意位置叶绿素的含量，并通过伪彩手段描述叶绿素含量的分布。研究结果表明，利用高光谱图像技术分析黄瓜叶片叶绿素含量及其在叶面上的分布是可行的。另外，该研究确定的最优植被指数所包含的695～705和750～800 nm 2个波段可用于搭建更加简便实用的快速检测叶片叶绿素的便携式多光谱设备。
- 叶绿素 /
- 光谱图像 /
- 线性回归 /
- 植被指数 /
- 叶绿素分布
Abstract: Abstract: The content and distribution of chlorophyll in leaves are important indicators of nutrition information in plants. The objective of this study was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation and distribution in leaves by using hyperspectral images. The hyperspectral imaging data cube of cucumber (Cucumissativus) leaves in the range of 450-850 nm were selected and preprocessed. A rectangle mesophyll about 100×200 pixels between the second and the third branch left of the main vein was selected as the region of interest (ROI). Spectra information of characteristic bands was extracted and used to set a model with measured chlorophyll content (spectra region extracted corresponding to region chlorophyll measured). The existing modeling methods, such as artificial neural networks (ANN), support vector machines (SVM), etc., can be used to achieve better results but are inconvenient for online applications due to the introduction of sophisticated algorithms. As an operation result of multiple spectrum values (addition, subtraction, multiplication, and division, combined with linear or nonlinear ways), vegetation indices, which play a role in indicating growth and biomass of vegetation, are significant in simplifying the model. Eight representative optical indices (or signatures), which were proposed as a function of the associated reflectance (Rλ) at the special wavelength (λ) nm, were used to predict the total chlorophyll content in cucumber leaves. Finally, (R695-705)?1?(R750-800)?1was identified as an optimum index, predicting the content of chlorophyll fairly well. The correlation coefficients of each model for calibration data set (rc) and validation data set (rp) were 0.8410 and 0.8286, while RMSEC (root mean square error of calibration) and RMSEP (root mean square error of predication) were the smallest (0.2045 mg/g and 0.2190 mg/g). The optimal model showed good stability and robustness due to two major advantages, namely the effects of "red edge" and baseline removal. On one hand, two feature bands (695-705 and 750-800 nm) of the model can be used to develop a kind of portable multispectral device. On the other hand, according to the model, chlorophyll content of the leaf was estimated at every pixel. A pseudo-color map was used to describe the law of chlorophyll distribution. On the map, it is evident that the content of chlorophyll is more in the mesophyll around the veins than in the veins. The edge is seen as less than the middle of the leaf, which is consistent with the actual distribution in the leaf. That is to say, it is a feasible analysis of chlorophyll content and distribution in cucumber leaves via the technique of hyperspectral images. Our results indicated that hyperspectral imaging was considerable for predicting chlorophyll content in leaves, thus allowing the chlorophyll content to be non-destructively detected in situ in living plant samples. In addition, the distribution map can also be used to analyze the accumulation of chlorophyll in spatial plants. Besides, it is easy to facilitate monitoring distribution and variation of chlorophyll in the tissues of plants. Further studies will provide a reliable way for processes that use photosynthetic pigments to participate in such as biochemical pathway, plant growth, and mechanisms of aging.
- chlorophyll /
- spectrographs /
- linear regression /
- leaf index /
- chlorophyll distribution

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参考文献(31)

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基于高光谱图像的黄瓜叶片叶绿素含量分布检测

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Detection of chlorophyll content distribution in cucumber leaves based on hyperspectral imaging

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