As crops are permanently threatened by pests and pathogens, breeding of resistant varieties is an important strategy to control these risks. During the breeding processes, an effective and reliable evaluation of promising candidates is necessary, but often difficult and laborious; therefore, a sensor-based method was used, revealing spatial and temporal differences in Cercospora leaf spot resistance of sugar beet lines with closely related genetic backgrounds. The method proved to be highly sensitive to quantitative differences in resistance and may improve resistance breeding.

Micro-scale phenotyping analysis of vascular bundles is valuable for phenotypic identification of germplasm resources. We developed a sample preparation protocol for micro-CT imaging of corn stalks, and designed an automatic image processing pipeline for phenotyping analysis of vascular bundles. These methods have potential to improve the throughput and quality of micro-scale phenotypic traits, and are expected to be useful in genetic and physiological studies to discover links between stalk anatomy and functions such as water transportation efficiency, mechanical properties.

Phenotyping is the most time consuming step in the process of breeding new plants for increased pathogen resistance: this could be improved through hyperspectral imaging. During experiments with different barley lines under pathogen pressure the novel technique of transmission based spectral imaging measurement was evaluated and compared with reflection based spectral imaging. The results of this study provide basic information about strengths and weaknesses of different hyperspectral measuring methods to be considered in future work.

Plant phenotyping platforms allow high-throughput experiments, and facilitate the study of plant growth to precisely monitored watering conditions. This study describes the disturbing effect of pot size on oilseed rape responses to water deficit. Our results raise the awareness of the need to carefully consider the pot size when designing protocols of high-throughput phenotyping experiments.

Infrared thermography for monitoring changes in leaf temperature as a consequence of reduced transpiration due to daytime stomatal closure in crassulacean acid metabolism (CAM) plants provides a rapid, non-invasive and economically attractive alternative to conventional gas exchange measurements for detecting CAM. Here we demonstrate the use of infrared (IR) thermography in the facultative CAM plant Mesembryanthemum crystallinum and show how it can be used to detect CAM in previously unstudied species.

The next step in wheat climate change adaptation research is to evaluate responses of individual cultivars to elevated CO₂. This will require the evaluation of large numbers of genotypes, and for practical reasons, preliminary studies are most likely to be conducted in controlled environments with container-grown plants. However, this might create problems or reduce the ability to detecting true cultivar responses.

The need for increased crop yields is becoming urgent as the amount of arable land available is reduced and environmental factors worsen, however, plant phenotyping has been identified as a key bottleneck in the process of improving crop yields. Here we review approaches to 3D shoot reconstruction to improve phenotyping using image-based methods. An automated system capable of producing three-dimensional (3D) models of plants would significantly aid phenotyping practice, increase accuracy and repeatability of measurements and potentially aid the process of improved crop yields.

Non-invasive root phenotyping is challenging. We introduce GrowScreen-PaGe, a rapid, cost-effective and high-resolution method for non-invasive, high-throughput phenotyping based on flat germination paper for studying root system traits and growth dynamics of crop plants. We demonstrate that this platform can be used to estimate root traits that reliably capture heritable diversity between genotypes and species.

Water limitation is known to affect plant growth and yield. To begin dissecting time-dependent effects of water limitation in Arabidopsis, we combined high-throughput phenomics and ionomics. These two approaches allowed us to quantify the negative effects of water limitation at critical points during plant development.

Breeding for stress tolerant crops is a highly challenging task. In this review we suggest four key components to be considered in pre-field screens (phenotyping) for complex, quantitative, traits under drought conditions. Moreover, we emphasise the advantages in using non-imaging, physiology-based, high-throughput phenotyping systems and diagnostic models as a preferred way to understand and characterise plant response to stress conditions.

In this study we aimed to better understand the relations between the external phenotype of young sugar beets and corresponding physiological and metabolic processes under drought stress and rewatering, ultimately providing tools to improve phenotypic approaches for drought tolerance. Using physiological, biochemical and a non-destructive phenotyping method, distinct stress phases and differential speed of recovery for selected physiological and metabolic processes were distinguished. Combination of these methods might be used to speed up the selection of drought-adapted cultivars in breeding programs.

Specific physiological traits are considered reliable indicators of salinity tolerance of wheat (Triticum aestivum L.) cultivars. As an alternative, spectral sensing is sufficiently sensitive to differentiate cultivars for differences in salinity tolerance, with considerable potential for high-throughput screening of phenotypic traits associated with this tolerance.

Crop phenotyping is a significant bottleneck of research in breeding and precision agriculture, which demands rapid data acquisition in the field. We established a cable-suspended phenotyping platform covering a field of 1 ha. The platform facilitates continuous analysis of several crops with multiple sensors throughout the year and provides reference data for mobile phenotyping platforms such as drones.

It is challenging to efficiently and accurately estimate ground cover, which is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. Here we compared two methods to estimate plot-level ground cover for three species automatically from UAV captured images. The method is suitable for high throughput phenotyping for applications in agronomy, physiology and breeding for different crop species.