A Machine Learning Model for Photorespiration Response to Multi-Factors

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

Construction of a photosynthetic rate prediction model for greenhouse strawberries with distributed regulation of light environment

In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.

Diurnal Change of the Photosynthetic Light-Response Curve of Buckbean (Menyanthes trifoliata), an Emergent Aquatic Plant

Understanding plant physiological responses to high temperature is an important concern pertaining to climate change. However, compared with terrestrial plants, information about aquatic plants remains limited. Since the degree of midday depression of photosynthesis under high temperature depends on soil water conditions, it is expected that emergent aquatic plants, for which soil water conditions are always saturated, will show different patterns compared with terrestrial plants. We investigated the diurnal course of the photosynthetic light-response curve and incident light intensity for a freshwater emergent plant, buckbean (Menyanthes trifoliata L.; Menyanthaceae) in a cool temperate region. The effect of midday depression was observed only on a very hot day, but not on a moderately hot day, in summer. The diurnal course of photosynthetic light-response curves on this hot day showed that latent morning reduction of photosynthetic capacity started at dawn, preceding the apparent depression around the midday, in agreement with results reported in terrestrial plants. We concluded that (1) midday depression of emergent plants occurs when the stress intensity exceeds the species' tolerance, and (2) measurements of not only photosynthetic rate under field conditions but also diurnal course of photosynthetic light-response curve are necessary to quantify the effect of midday depression.