Exploring relationships between canopy architecture, light distribution and photosynthesis in contrasting rice genotypes using 3D canopy reconstruction

The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties - four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64) - was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate (Pmax) throughout canopy depth, hypothesising that light is the sole determinant of productivity in these conditions. First we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal Pmax responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However measured Pmax, especially in lower layers, was consistently higher than the optimal Pmax indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency

Sunfleck properties from time series of fluctuating light

Light in canopies is highly dynamic since the strength and composition of incoming radiation is determined by the wind and the Sun's trajectory and by canopy structure. For this highly dynamic environment, we mathematically defined sunflecks as periods of high irradiance relative to the background light environment. They can account for a large proportion of the light available for photosynthesis. Based on high-frequency irradiance measurements with a CCD array spectroradiometer, we investigated how the frequency of measurement affects what we define as sunflecks. Do different plant canopies produce sunflecks with different properties? How does the spectral composition and strength of irradiance in the shade vary during a sunfleck? Our results suggest that high-frequency measurements improved our description of light fluctuations and led to the detection of shorter, more frequent and intense sunflecks. We found that shorter wind-induced sunflecks contribute most of the irradiance attributable to sunflecks, contrary to previous reports from both forests and crops. Large variations in sunfleck properties related to canopy depth and species, including distinct spectral composition under shade and sunflecks, suggest that mapping canopy structural traits may help us model photosynthesis dynamically.Peer reviewe

Casting light on the architecture of crop yield

Crop canopy architecture is a central component of yield. The arrangement of leaves in three-dimensional space defines the efficiency of absorption of radiation and its conversion into dry matter at the canopy level. The description of architecture is normally associated with light since the optimal distribution of light is associated with that of other essential components such as nitrogen and pigments. However, architecture has been influenced by a number of other unrelated processes through breeding and selection that may have acted independently or even against light use efficiency. This review attempts to provide a broad view and interpretation of canopy architectural properties and the factors affecting crop architecture starting with evolution, domestication, climatic conditions and cultivation patterns, predominantly focusing on field grown agricultural crops. Using examples of modelling with a virtual canopy, we will discuss how architectural traits affect light interception and photosynthesis. Finally, we will discuss the future of architectural research: the concept of the ideal plant type (the ideotype) and which features we can expect to see, as well as the social constraints that may govern future crop architecture

Application of deep learning for the analysis of stomata: A review of current methods and future directions

Plant physiology and metabolism relies on the function of stomata, structures on the surface of above ground organs, which facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore which make up the stomata, as well as the density (number per unit area) are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopes, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used; from data acquisition, pre-processing, DL architecture and output evaluation to post processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimise uptake; predominantly focusing on the sharing of datasets and generalisation of models as well as the caveats associated with utilising image data to infer physiological function