Smartphone near infrared monitoring of plant stress
Smartphone near infrared monitoring of plant stress
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DOI：
10.1016/j.compag.2018.08.046
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发表时间：
2018-11-01
影响因子：
8.3
通讯作者：
Yoon, Jeong-Yeol
中科院分区：
文献类型：
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作者：
Chung, Soo;Breshears, Lane E.;Yoon, Jeong-Yeol
最广泛使用的植物胁迫监测方法是使用近红外 (NIR) 分光光度法来计算归一化植被指数 (NDVI)，定义为 [NIR 反射率红反射率]/[NIR 反射率 + 红反射率]。 NDVI 测量红色光谱中叶绿素的吸收相对于近红外中细胞结构的散射，并已用于监测植被健康状况以及随后通过航空或卫星图像监测其压力。我们没有使用相当昂贵的近红外分光光度计或近红外相机，而是尝试使用商用智能手机，利用其（可能是无意的）识别近红外 (NIR) 颜色的能力。一些最新版本的智能手机已经取消了相机上的 NIR 滤光片，并且能够识别 CMOS 阵列的红色像素中的 NIR。通过将廉价的 800 nm 高通滤波器连接到智能手机摄像头，我们能够收集 MR 反射率（带高通光学滤波器）和红色反射率（不带滤波器），从而实现 NDVI 评估。该方法通过测量一系列叶绿素溶液的 NDVI 值进行了验证，并表现出很强的线性相关性，R-2 = 0.948，证实了智能手机评估 NDVI 的能力。使用来自三种不同植物物种的叶子，使用智能手机评估 NDVI 值，并使用丙酮提取和随后的分光光度测定法与植物的叶绿素含量进行比较。发现良好的线性关系，R-2 = 0.88-0.92。我们进一步评估了 NDVI 值与植物含水量（通过烘干测量）的关系，显示出与 NDVI 饱和度高于 50% 含水量的非线性关系。测定时间几乎是瞬时的，只需要智能手机和高通滤波器，因此可以廉价、易于使用、快速且早期地预测植物胁迫，可用于田间和家庭应用。
The most widely used method for monitoring plant stress is the use of near infrared (NIR) spectrophotometry to calculate normalized difference vegetation index (NDVI), as defined by [NIR reflectance red reflectance]/[NIR reflectance + red reflectance]. NDVI measures the chlorophyll absorption in the red spectrum relative to the scattering by cellular structure in NIR, and has been used to monitor vegetation health and subsequently its stress from aerial or satellite images. Rather than using an NIR spectrophotometer or an NIR camera that is rather expensive, we attempted to use a commercial smartphone, utilizing its (potentially unintended) ability in recognizing near infrared (NIR) color. Some of the most recent versions of smartphones have eliminated the NIR block filters on their cameras, and able to recognize NIR in their red pixels of CMOS array. Through attaching an inexpensive high pass filter at 800 nm to a smartphone camera, we were able to collect the MR reflectance (with a high pass optical filter) and the red reflectance (without a filter), enabling NDVI assessments. This method was verified by measuring the NDVI values from a series of chlorophyll solutions, and showed a strong linear correlation with R-2 = 0.948, corroborating the smartphone's ability in evaluating NDVI. Using the leaves from three different plant species, the NDVI values were evaluated using the smartphone and compared with the plants' chlorophyll contents using acetone extraction and subsequent spectrophotometry. A good linear relationship was found with R-2 = 0.88-0.92. We further evaluated the NDVI values against the plants' water contents (measured by oven-drying), showing the non-linear relationship with the NDVI saturation above 50% water content. The assay time was almost instantaneous, requiring only a smartphone and a high pass filter, thus allowing inexpensive, easy-to-use, rapid, and early prediction of plant stress that can be used for field and household applications.