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

基于人工智能的光合作用建模，作为辐射强度、波长和脉冲调制的函数。

• 批准号: 500805487
• 负责人: Professor Dr. Ralf Kaldenhoff
• 金额: --
• 依托单位: Fachgebiet für Adaptive Lichttechnische Systeme und Visuelle Verarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/500805487?language=en
• 关键词: AI; based; modeling; photosynthesis; function

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.

光合作用被认为是一个很好的描述过程。它是在分子基础上定义的，许多影响，尤其是光的影响，似乎都是众所周知的。这些发现被用来建立数学的、单因素或多因素的模型，以预测在各种规定的光照条件下的光合作用表现。所建立的模型只适用于给定的条件，而这些条件并不一定总是与实际情况相符。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)使用这些技术的可能性，这些技术仅在最近几年才可用，用于光合作用的研究，b)将建立一个描述光合作用有效性的新的整体模型。与传统模型不同的是，它不能将单个因素整合到数学模型中。相反，整个植物系统被认为是一个黑盒子，结果数据通过大数据、人工智能、深度学习等在光合作用的多变量模型中表示。这个模型是修改过的，是可变的。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）和光照历史。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。有效的人工智能算法是根据高通量筛选的数据开发的。在模型开发后，对不同发育阶段的特定植物物种进行验证，并确定耐受性范围。在有针对性的基于人工智能的模型中对光合作用的描述将改变我们对这个对我们所有人都非常重要的过程的印象，完善它，并使创造一个新的整体图景成为可能。该模式将对植物种植的所有领域产生影响；包括农业和温室文化。一般可以通过服务器数据库提供和科学出版物访问它。