Alem Gebremedhin

The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.

Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R²) = 0.67–0.68 and a root mean square error (RMSE) between 5.43–7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59–5.44 g and 22–28%, respectively. For the FHY, R² values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between ~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs.

Increasing the yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. Breeding for high dry matter yield (DMY) in forage crops is likely to be enhanced with the development of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Therefore, phenotyping remains a critical bottleneck in the implementation of GS in forage species. Assessments of DMY in forage crop breeding include visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground- and aerial-based platforms equipped with advanced sensors offer opportunities for fast, nondestructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. The workflow of image acquisition, processing and analysis are reviewed. The “big data” challenges, proposed storage and management techniques, development of advanced statistical tools and methods for incorporating the HTP into forage breeding systems are also reviewed. Initial results where these techniques have been applied to forages have been promising but further research and development is required to adapt them to forage breeding situations, particularly with respect to the management of large data sets and the integration of information from spaced plants to sward plots. However, realizing the potential of sensor technologies combined with GS leads to greater rates of genetic gain in forages.

Summary: We present a simple and effective high‐throughput experimental platform for simultaneous and continuous monitoring of water relations in the soil–plant–atmosphere continuum of numerous plants under dynamic environmental conditions. This system provides a simultaneously measured, detailed physiological response profile for each plant in the array, over time periods ranging from a few minutes to the entire growing season, under normal, stress and recovery conditions and at any phenological stage. Three probes for each pot in the array and a specially designed algorithm enable detailed water‐relations characterization of whole‐plant transpiration, biomass gain, stomatal conductance and root flux. They also enable quantitative calculation of the whole plant water‐use efficiency and relative water content at high resolution under dynamic soil and atmospheric conditions. The system has no moving parts and can fit into many growing environments. A screening of 65 introgression lines of a wild tomato species (Solanum pennellii) crossed with cultivated tomato (S. lycopersicum), using our system and conventional gas‐exchange tools, confirmed the accuracy of the system as well as its diagnostic capabilities. The use of this high‐throughput diagnostic screening method is discussed in light of the gaps in our understanding of the genetic regulation of whole‐plant performance, particularly under abiotic stress.

Vacuolar water movement is largely controlled by membrane channels called tonoplast-intrinsic aquaporins (TIP-AQPs). Some TIP-AQP genes, such as TIP2;2 and TIP1;1, are up-regulated upon exposure to biotic stress. Moreover, TIP1;1 transcript levels are higher in leaves of a tomato (Solanum lycopersicum) line resistant to Tomato yellow leaf curl virus (TYLCV) than in those of a susceptible line with a similar genetic background. Virus-induced silencing of TIP1;1 in the tomato resistant line and the use of an Arabidopsis (Arabidopsis thaliana) tip1;1 null mutant showed that resistance to TYLCV is severely compromised in the absence of TIP1:1. Constitutive expression of tomato TIP2;2 in transgenic TYLCV-susceptible tomato and Arabidopsis plants was correlated with increased TYLCV resistance, increased transpiration, decreased abscisic acid levels, and increased salicylic acid levels at the early stages of infection. We propose that TIP-AQPs affect the induction of leaf abscisic acid, which leads to increased levels of transpiration and gas exchange, as well as better salicylic acid signaling.

Water scarcity is a critical limitation for agricultural systems. Two different water management strategies have evolved in plants: an isohydric strategy and an anisohydric strategy. Isohydric plants maintain a constant midday leaf water potential (Ψleaf) when water is abundant, as well as under drought conditions, by reducing stomatal conductance as necessary to limit transpiration. Anisohydric plants have more variable Ψleaf and keep their stomata open and photosynthetic rates high for longer periods, even in the presence of decreasing leaf water potential. This risk-taking behavior of anisohydric plants might be beneficial when water is abundant, as well as under moderately stressful conditions. However, under conditions of intense drought, this behavior might endanger the plant. We will discuss the advantages and disadvantages of these two water-usage strategies and their effects on the plant’s ability to tolerate abiotic and biotic stress. The involvement of plant tonoplast AQPs in this process will also be discussed.