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.

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.

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses:Extending the Concept of Thermal Time in Field Crops

Predicting plant development,a longstanding goal in plant physiology,involves 2 interwoven components:continuous growth and the progression of growth stages(phenology).Current models for winter wheat and soybean assume species-level growth responses to temperature.We challenge this assumption,suggesting that cultivar-specific temperature responses substantially affect phenology.To investigate,we collected field-based growth and phenology data in winter wheat and soybean over multiple years.We used diverse models,from linear to neural networks,to assess growth responses to temperature at various trait and covariate levels.Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions.With cultivar-specific models,additional relations to other stressors than temperature were found.The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies,which will deepen our understanding of key factors that influence plant development.Consequently,this work has implications for crop breeding and cultivation under adverse climatic conditions.