Khruschev S S, Plyusnina T Yu, Antal T K, Pogosyan S I, Riznichenko G Yu, Rubin A B
Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia.
Laboratory of Integrated Environmental Research, Pskov State University, Pskov, 180000 Russia.
Biophys Rev. 2022 Aug 10;14(4):821-842. doi: 10.1007/s12551-022-00982-2. eCollection 2022 Aug.
Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.
监测自然和人工生物群落的光合活性至关重要。光合作用是地球上生命存在的基础，人为影响导致的初级光合产量下降可能会产生灾难性后果。目前，人们正在大力研发能够持续监测陆地植物和微藻光合机构状态的技术。有多种信息来源适用于评估光合活性，包括气体交换和光学（反射率和荧光）测量。廉价光学传感器的出现使得在本地（手动或使用自主海陆监测站）和全球范围（使用飞机和卫星成像）收集数据成为可能。在本综述中，我们考虑了基于本地和远程光学测量（高光谱成像、太阳诱导叶绿素荧光、局部叶绿素荧光成像以及各种快速和延迟叶绿素荧光诱导技术）来确定光合作用功能参数的机器学习方法。这些方法包括经典回归方法和新型回归方法（如偏最小二乘法）、无监督聚类分析技术、各种分类方法（支持向量机、随机森林等）以及人工神经网络（多层感知器、长短期记忆网络等）。我们还考虑了时间序列分析的特殊方面。讨论了特定信息源和数学方法在评估水质和预测藻华、估计生物群落的初级生产力、农业植物的胁迫耐受性等方面的适用性。
