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Background: Genetic studies and breeding of agricultural crops frequently involve phenotypic characterization of large collections of genotypes grown in field conditions. These evaluations are typically based on visual observations and manual (destructive) measurements. Robust image capture and analysis procedures that allow phenotyping large collections of genotypes in time series during developmental phases represent a clear advantage as they allow non-destructive monitoring of plant growth and performance. A L. perenne germplasm panel including wild accessions, breeding material and commercial varieties has been used to develop a low-cost, high-throughput phenotyping tool for determining plant growth based on images of individual plants during two consecutive growing seasons. Further we have determined the correlation between image analysis-based estimates of the plant's base area and the capacity to regrow after cutting, with manual counts of tiller number and measurements of leaf growth 2 weeks after cutting, respectively. When working with field-grown plants, image acquisition and image segmentation are particularly challenging as outdoor light conditions vary throughout the day and the season, and variable soil colours hamper the delineation of the object of interest in the image. Therefore we have used several segmentation methods including colour-, texture- and edge-based approaches, and factors derived after a fast Fourier transformation. The performance of the procedure developed has been analysed in terms of effectiveness across different environmental conditions and time points in the season. Results: The procedure developed was able to analyse correctly 77.2 % of the 24,048 top view images processed. High correlations were found between plant's base area (image analysis-based) and tiller number (manual measurement) and between regrowth after cutting (image analysis-based) and leaf growth 2 weeks after cutting (manual measurement), with r values up to 0.792 and 0.824, respectively. Nevertheless, these relations depend on the origin of the plant material (forage breeding lines, current forage varieties, current turf varieties, and wild accessions) and the period in the season. Conclusions: The image-derived parameters presented here deliver reliable, objective data, complementary to the breeders' scores, and are useful for genetic studies. Furthermore, large variation was shown among genotypes for the parameters investigated. [ABSTRACT FROM AUTHOR]

In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile. [ABSTRACT FROM AUTHOR]

In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile.