Combining High-Resolution Imaging,Deep Learning,and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies

Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling.Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions.Besides direct effects on green leaf area in terms of leaf damage,stressors often anticipate or accelerate physiological senescence,which may multiply their negative impact on grain filling.Here,we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots(stems+leaves)based on deep learning models for semantic segmentation and color properties of vegetation.A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks,which greatly reduced the risk of annotation uncertainties and annotation effort.Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis.Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations(r≈0.9).Contrasting patterns were observed for plots with different levels of foliar diseases,particularly septoria tritici blotch.Our results suggest that tracking the chlorotic and necrotic fractions separately may enable(a)a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and(b)investigation of interactions between biotic stress and physiological senescence.The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

Frost Damage Index:The Antipode of Growing Degree Days

Abiotic stresses such as heat and frost limit plant growth and productivity.Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence.Winter crops show senescence caused by cold spells,visible as declines in leaf area.We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field.Thirty-six winter wheat genotypes were measured in multiple years.A concept termed"frost damage index"(FDI)was developed that,in analogy to growing degree days,summarizes frost events in a cumulative way.The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness.The FDI concept could be adapted to other factors such as drought or heat stress.While commonly not considered in plant growth modeling,integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

The Leaf Reticulate Mutant dovl Is Impaired in the First Step of Purine Metabolism

A series of reticulated Arabidopsis thaliana mutants were previously described. All mutants show a reticulate leaf pattern, namely green veins on a pale leaf lamina. They have an aberrant mesophyll structure but an intact layer of bundle sheath cells around the veins. Here, we unravel the function of the previously described reticulated EMS-mutant dovl （differential development of vascular associated cells 1）. By positional cloning, we identified the mutated gene, which encodes glutamine phosphoribosyl pyrophosphate aminotransferase 2 （ATase2）, an enzyme catalyzing the first step of purine nucleotide biosynthesis, dovl is allelic to the previously characterized cial-2 mutant that was isolated in a screen for mutants with impaired chloroplast protein import. We show that purine-derived total cytokinins are lowered in clovl and crosses with phytohormone reporter lines revealed differential reporter activity patterns in dovl. Metabolite profiling unraveled that amino acids that are involved in purine biosynthesis are increased in dovl. This study identified the mo- lecular basis of an established mutant line, which has the potential for further investigation of the interaction between metabolism and leaf development.

Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping.Nevertheless,methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare.We aimed to develop proxy measures for the rate of plant emergence,the number of tillers,and the beginning of stem elongation using drone-based imagery.We used RGB images(ground sampling distance of 3mm pixel-1)acquired by repeated flights(≥2 flights per week)to quantify temporal changes of visible leaf area.To exploit the information contained in the multitude of viewing angles within the RGB images,we processed them to multiview ground cover images showing plant pixel fractions.Based on these images,we trained a support vector machine for the beginning of stem elongation(GS30).Using the GS30 as key point,we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling,respectively.Our results show that determination coefficients of predictions are moderate for plant count(R^(2)=0:52),but strong for tiller count(R^(2)=0:86)and GS30(R^(2)=0:77).Heritabilities are superior to manual measurements for plant count and tiller count,but inferior for GS30 measurements.Increasing the selection intensity due to throughput may overcome this limitation.Multiview image traits can replace hand measurements with high efficiency(85-223%).We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.