AI-based modeling of photosynthesis as a function of radiation intensity, wavelengths, and - pulse modulation.

光合作用被认为是一个描述得很好的过程。它是在分子基础上定义的，许多影响，特别是光的影响，似乎足够广为人知。这些发现被用来创建数学的、基于单因素或多因素的模型，这些模型允许预测在各种定义的光条件下的光合作用表现。已建立的模型只适用于给定的条件，而不一定总是与真实的情况相对应。通过使用现代LED和基于人工智能的照明程序控制，我们将探索：a)将这些近年来才可用的技术用于光合作用研究的可能性；b)将建立一个描述光合作用效率的新的整体模型。与传统模型不同，它不能将单个因素整合到一个数学模型中。相反，整个植物系统被认为是一个黑匣子，结果数据通过大数据、人工智能、深度学习等手段在光合作用的多变量模型中表示。该模型是修正的和可变的。它受辐射强度、波长和脉冲调制的影响，反映了植物种类、植物年龄(发育阶段)和光历史。为此，创建了一种高通量测量方法，其中包括使用热像仪、反射和吸收测量、基于LED的可变照明和叶绿素荧光测量。有能力的人工智能算法是用来自高通量筛选的数据开发的。在模型开发后，对不同发育阶段的已定义植物物种进行了验证，并定义了耐受范围。在基于人工智能的目标模型中对光合作用的描述将改变我们对这个过程的印象，而这个过程对我们所有人来说都是如此重要，并对其进行改进，使之有可能创造出一幅新颖的整体图景。该模式将对植物种植的所有领域产生影响，包括农业和温室栽培。它一般将通过提供服务器数据库和科学出版物来获取。

Photosynthesis is considered a well-described process. It is defined at a molecular basis and many influences, especially those of light, seem sufficiently well known. The findings were used to create mathematical, single- or multi-factor-based models that allow to predict photosynthesis performance under various defined light conditions. The established models apply only under the given conditions, which do not necessarily always correspond to the real ones. By using modern LEDs and AI-based control of lighting programs we will explore, a) the possibility of using these techniques, that are only available in recent years, for the study of photosynthesis and b) a new holistic model describing photosynthesis effectiveness will be established. Unlike conventional models, it does not work with individual factors integrated into a mathematical model. Rather, the entire plant system is considered a black box and the resulting data is represented by means of big data, AI, deep learning etc. in a multi- variant model of photosynthesis. This model is modified and variable. It is influenced by radiation intensity, wavelengths and pulse modulation reflecting plant species, plant age (developmental stage) and light history. For this purpose, a high-throughput measurement method is created, which includes, among others, the use of thermal cameras, reflection and absorption measurement, LED-based variable illumination and chlorophyll fluorescence measurement. Competent AI algorithms are developed with data from high- throughput screening. After model development, these are validated for defined plant species at different stages of development and tolerance ranges are defined. The description of photosynthesis in the targeted AI-based model will change our image of the process that is so important to all of us, refine it, and make it possible to create a novel overall picture. The model will have an impact on all areas of plant cultivation; Agriculture and greenhouse culture included. It will be generally accessible through server-database provision and scientific publications.