Askey Bryce C, Dai Ru, Lee Won Suk, Kim Jeongim
Horticultural Sciences Department University of Florida Gainesville Florida 32611 USA.
Department of Agricultural and Biological Engineering University of Florida Gainesville Florida 32611 USA.
Appl Plant Sci. 2019 Nov 10;7(11):e11301. doi: 10.1002/aps3.11301. eCollection 2019 Nov.
When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible.
Twenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in leaves.
The digital imaging-based nature of this protocol makes it a low-cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large-scale, cost-effective systems for monitoring plant health.
当植物暴露于胁迫条件下时，可能会发生不可逆转的损害，对产量产生负面影响。因此，尽早检测植物中的胁迫症状，如花青素的积累，非常重要。
在五个颜色空间中训练了22个回归模型，以根据数字彩色成像数据建立植物花青素水平的预测模型。其中，使用标准红、绿、蓝（sRGB）颜色空间数据训练的分位数随机森林回归模型最准确地预测了实际花青素水平。然后使用该模型对叶片中花青素的时空积累进行无创监测。
该方案基于数字成像的特性使其成为一种低成本、无创的植物胁迫检测方法。将类似方案应用于更具经济可行性的作物，可能会开发出大规模、经济高效的植物健康监测系统。
