Cortazar Bingen, Koydemir Hatice Ceylan, Tseng Derek, Feng Steve, Ozcan Aydogan
Department of Electrical Engineering, University of California Los Angeles (UCLA), CA 90095, USA.
Lab Chip. 2015 Apr 7;15(7):1708-16. doi: 10.1039/c4lc01279h. Epub 2015 Feb 11.
Measuring plant chlorophyll concentration is a well-known and commonly used method in agriculture and environmental applications for monitoring plant health, which also correlates with many other plant parameters including, e.g., carotenoids, nitrogen, maximum green fluorescence, etc. Direct chlorophyll measurement using chemical extraction is destructive, complex and time-consuming, which has led to the development of mobile optical readers, providing non-destructive but at the same time relatively expensive tools for evaluation of plant chlorophyll levels. Here we demonstrate accurate measurement of chlorophyll concentration in plant leaves using Google Glass and a custom-developed software application together with a cost-effective leaf holder and multi-spectral illuminator device. Two images, taken using Google Glass, of a leaf placed in our portable illuminator device under red and white (i.e., broadband) light-emitting-diode (LED) illumination are uploaded to our servers for remote digital processing and chlorophyll quantification, with results returned to the user in less than 10 seconds. Intensity measurements extracted from the uploaded images are mapped against gold-standard colorimetric measurements made through a commercially available reader to generate calibration curves for plant leaf chlorophyll concentration. Using five plant species to calibrate our system, we demonstrate that our approach can accurately and rapidly estimate chlorophyll concentration of fifteen different plant species under both indoor and outdoor lighting conditions. This Google Glass based chlorophyll measurement platform can display the results in spatiotemporal and tabular forms and would be highly useful for monitoring of plant health in environmental and agriculture related applications, including e.g., urban plant monitoring, indirect measurements of the effects of climate change, and as an early indicator for water, soil, and air quality degradation.
测量植物叶绿素浓度是农业和环境应用中监测植物健康的一种广为人知且常用的方法，它还与许多其他植物参数相关，包括例如类胡萝卜素、氮、最大绿色荧光等。使用化学提取法直接测量叶绿素具有破坏性、操作复杂且耗时，这促使了移动光学阅读器的发展，为评估植物叶绿素水平提供了非破坏性但同时相对昂贵的工具。在此，我们展示了使用谷歌眼镜、定制开发的软件应用程序以及经济高效的叶夹和多光谱照明器设备，能够准确测量植物叶片中的叶绿素浓度。在红色和白色（即宽带）发光二极管（LED）照明下，使用谷歌眼镜拍摄置于我们便携式照明器设备中的叶片的两张图像，并上传到我们的服务器进行远程数字处理和叶绿素定量分析，结果在不到10秒内返回给用户。从上传图像中提取的强度测量值与通过市售阅读器进行的金标准比色测量值进行映射，以生成植物叶片叶绿素浓度的校准曲线。我们使用五种植物物种对系统进行校准，证明我们的方法能够在室内和室外照明条件下准确、快速地估计十五种不同植物物种的叶绿素浓度。这个基于谷歌眼镜的叶绿素测量平台可以以时空和表格形式显示结果，对于环境和农业相关应用中的植物健康监测非常有用，包括例如城市植物监测、气候变化影响的间接测量，以及作为水、土壤和空气质量退化的早期指标。
