Development of a minimized model structure and a feedback control framework for regulating photosynthetic activities

From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement

To keep up with the growth of human population and to circumvent deleterious effects of global climate change, it is essential to enhance crop yield to achieve higher production. Here we review mathematical models of oxygenic photosynthesis that are extensively used, and discuss in depth a subset that accounts for diverse approaches providing solutions to our objective. These include models (1) to study different ways to enhance photosynthesis, such as fine-tuning antenna size, photoprotection and electron transport; (2) to bioengineer carbon metabolism; and (3) to evaluate the interactions between the process of photosynthesis and the seasonal crop dynamics, or those that have included statistical whole-genome prediction methods to quantify the impact of photosynthesis traits on the improvement of crop yield. We conclude by emphasizing that the results obtained in these studies clearly demonstrate that mathematical modelling is a key tool to examine different approaches to improve photosynthesis for better productivity, while effective multiscale crop models, especially those that also include remote sensing data, are indispensable to verify different strategies to obtain maximized crop yields.

Detection of antibiotic and microplastic pollutants in Chrysanthemum coronarium L. based on chlorophyll fluorescence

Large amounts of antibiotics and microplastics are used in daily life and agricultural production, which affects not only plant growth but also potentially the food safety of vegetables and other plant products. Fast detection of the presence of antibiotics and microplastics in leafy vegetables is of great interest to the public. In this work, a method was developed to detect sulfadiazine and polystyrene, commonly used antibiotics and microplastics, in vegetables by measuring and modeling photosystem II chlorophyll a fluorescence (ChlF) emission from leaves. Chrysanthemum coronarium L., a common beverage and medicinal plant, was used to verify the developed method. Scanning electron microscopy, transmission electron microscopy, and liquid chromatograph-mass spectrometer analysis were used to show the presence of the two pollutants in the samples. The developed kinetic model could describe measured ChlF variations with an average relative error of 0.6%. The model parameters estimated for the chlorophyll a fluorescence induction kinetics curve (OJIP) induction can differentiate the two types of stresses while the commonly used ChlF OJIP induction characteristics cannot. This work provides a concept to detect antibiotic pollutants and microplastic pollutants in vegetables based on ChlF.

An open Internet of Things (IoT)-based framework for feedback control of photosynthetic activities

Active control of photosynthetic activities is important in plant physiological study. Although models of plant photosynthesis have been built at different scales, they have not been fully examined for their application in plant growth control. However, we do not have an infrastructure to support such experiments since current plant growth chambers usually use fixed control protocols. In our current paper, an open IoT-based framework is proposed. This framework allows a plant scientist or agricultural engineer, through an application programming interface (API), in a desirable programming language, (1) to gather environmental data and plant physiological responses; (2) to program and execute control algorithms based on their models, and then (3) to implement real-time commands to control environmental factors. A plant growth chamber was developed to demonstrate the concept of the proposed open framework.