Abstract

BackgroundGenetic 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.ResultsThe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-016-0132-8) contains supplementary material, which is available to authorized users.

Highlights

  • Genetic studies and breeding of agricultural crops frequently involve phenotypic characterization of large collections of genotypes grown in field conditions

  • No single segmentation method resulted in a satisfactory number of images that were correctly analysed over all series, because of the large variation in light quality and intensity, background and plant characteristics among the different series of images

  • We combined the masks of eight segmentation methods into a composite mask to improve the robustness of the overall image analysis procedure

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Summary

Introduction

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. Regrowth is usually evaluated by visual inspection of the plants a few days or weeks after mowing, without any reference to the status of the plant just before or after cutting It is usually not known whether a good score is due to a high capacity to resume growth from tillers already formed before cutting, or by the formation of new tillers in the periphery of the plant. Methodologies that allow a higher level of resolution and precision in the determination of growth-related characteristics are required

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