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

Plant physiology and metabolism rely on the function of stomata, structures on the surface of above-ground organs that facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore that 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 microscopy, traditionally relying on time-consuming manual analysis.

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, , calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone.

The effect of canopy architecture on the patterning of "windflecks" within a wheat canopy.

Under field conditions, plants are subject to wind-induced movement which creates fluctuations of light intensity and spectral quality reaching the leaves, defined here as windflecks. Within this study, irradiance within two contrasting wheat (Triticum aestivum) canopies during full sun conditions was measured using a spectroradiometer to determine the frequency, duration and magnitude of low- to high-light events plus the spectral composition during wind-induced movement. Similarly, a static canopy was modelled using three-dimensional reconstruction and ray tracing to determine fleck characteristics without the presence of wind.

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in most agricultural systems, yet the complexity of the field environment means that it remained understudied. Despite the ready availability of image sequences showing plant motion, the cultivation of crop plants in dense field stands makes it difficult to detect features and characterize their general movement traits.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling.

Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach.

A canopy conundrum: can wind-induced movement help to increase crop productivity by relieving photosynthetic limitations?

Wind-induced movement is a ubiquitous occurrence for all plants grown in natural or agricultural settings, and in the context of high, damaging wind speeds it has been well studied. However, the impact of lower wind speeds (which do not cause any damage) on mode of movement, light transmission, and photosynthetic properties has, surprisingly, not been fully explored. This impact is likely to be influenced by biomechanical properties and architectural features of the plant and canopy.

Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction.

Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the construction of accurate 3D plant models is challenging, as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition.

Approaches to three-dimensional reconstruction of plant shoot topology and geometry.

There are currently 805million people classified as chronically undernourished, and yet the World's population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate.