Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

Abstract

Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications.

Genotyping by sequencing for SNP discovery and genetic mapping of resistance to race 1 of Fusarium oxysporum in watermelon

“…23 DAI) if data will only be collected at a single time point. In future, the modified-tray dip method could be combined with precision phenotyping (Barbedo 2014;Cobb et al, 2013;Fiorani and Schurr 2013;Furbank and Tester, 2011;Junker et al, 2015;Montes et al, 2007) using image sensors for more accurate and objective disease severity ratings (Mahlein, 2015). However, the growth habit of watermelon presents real challenges for imaging systems since it is difficult to keep individual plants in seedling trays or small pots separated as they grow trailing vines.…”

Phenotyping roots in darkness: disturbance-free root imaging with near infrared illumination

“…Furthermore, the use of the filter material allows for a higher throughput since it avoids time needed for mechanical removal of a light cover plate from the rhizotrons for imaging and potential effects of visible light on root growth during the imaging process. The different components of the presented root imaging concept are suitable for integration into existing high throughput shoot phenotyping systems set up at IPK (Junker et al 2015) or elsewhere, thereby supporting automation and upscaling of root phenotyping and enabling the integrated analyses of shoot and root growth and developmental dynamics. However, we would suggest using sufficient replication when applying this method for highthroughput root phenotyping.…”

“…The main objective of this work was to test and validate a root phenotyping concept suitable to be integrated into an existing high-throughput phenotyping platform hitherto only set up for shoot trait assessment (Junker et al 2015). In this respect, the following questions were addressed for four representative model and crop plant species (Arabidopsis, rapeseed, barley and maize).…”

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

“…Recently, some high-throughput plant phenotyping platforms (Reuzeau et al, 2005;Nagel et al, 2012;Honsdorf et al, 2014) and open-source image-analysis pipelines (Hartmann et al, 2011;Chen et al, 2014;Klukas et al, 2014) were developed to quantify phenotypic traits at the population level for different plant species. High-throughput noninvasive phenotyping also has been adopted successfully to assess the genetics of estimated biomass dynamics in maize (Junker et al, 2015;Muraya et al, 2017). In a previous work, a rice automatic phenotyping platform (RAP) was designed to achieve high-throughput screening of rice (Oryza sativa) plants for genetic studies (Yang et al, 2014).…”