phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.Seeds play a major role in keeping continuity between successive generations (Esau, 1977) and are key for the distribution and evolution (Moles et al., 2005) of higher plants. Fertile seeds carry an embryo and may contain nutrient storage tissues in cotyledons, endosperm, and/or perisperm, supporting germination and seedling development at early developmental stages. Although this is true for all seed plants, various traits of seeds, such as size, shape, weight, and chemical composition, can be very different between plant species or accessions. For example, the Arabidopsis (Arabidopsis thaliana) accession Cape Verde Islands was reported to yield on average 40% fewer seeds than Landsberg erecta, but they are almost twice as heavy (Alonso-Blanco et al., 1999). Considering today’s plant species, single-seed mass may vary over a range of 11.5 orders of magnitude (Moles et al., 2005). Seed mass is under strong genetic control, whereas the total number of seeds of a plant is largely affected by the environment (Paul-Victor and Turnbull, 2009). It has been demonstrated that the size, mass, and shape of Arabidopsis seeds may be regulated by brassinosteroid (Jiang et al., 2013), and it was shown recently that seed size in rice (Oryza sativa) can be influenced by the epiallele Epi-rav6 (Zhang et al., 2015). The ability of plants to switch between small and larger seeds may be understood as an adaptation to novel environments (Igea et al., 2016). However, it is still not fully understood whether, or to what extent, the variability of seed traits within plant species or genotypes has an impact on the development and further performance of a plant.When comparing biometric seed data of different dimensions such as length (one-dimensional), projected area (two-dimensional [2D]), or volume and mass (both three-dimensional [3D]), one can argue that mass is the most relevant parameter as a proxy for the amount of reserves a seed provides for the offspring. This might be true even when considering that the type of reserves, such as proteins, carbohydrates, or lipids (Rolletschek et al., 2015), and also different seed tissues, such as seed coat, embryo, or endosperm, may contribute differently to seed mass (Alonso-Blanco et al., 1999). While seed mass and time to germination (radicle protrusion) do not necessarily correlate (Norden et al., 2009), in particular under greenhouse conditions, higher seed mass may be advantageous for seedling establishment under adverse environmental conditions (Moles et al., 2005). For example, shade-tolerant species showed largely higher seed masses than cogeneric species growing in open habitats, indicating that seedlings under low-light conditions need more reserves than under good light (Salisbury, 1974). Seedlings of wild radish (Raphanus raphanistrum) emerged more likely from heavier seeds than from small seeds under field conditions but not in the greenhouse (Stanton, 1984), and for Arabidopsis, seed mass was reported to be higher in populations growing naturally at higher altitudes taken as a proxy for harsher conditions (Montesinos-Navarro et al., 2011).Seed mass can be measured individually (Stanton, 1984), but it is generally collected as an average value of batches of 50 to 1,000 seeds (Jako et al., 2001; Jofuku et al., 2005; Montesinos-Navarro et al., 2011; Tanabata et al., 2012). Alternatively, 2D scans are analyzed to determine parameters such as seed length, width, area, and perimeter length as a measure for seed size (Tanabata et al., 2012). This approach can be implemented in high-throughput facilities to obtain projected areas of seed grains combined with genome-wide association studies (Yang et al., 2014). Although projected seed area can easily be measured with a common office scanner (Herridge et al., 2011; Tanabata et al., 2012; Moore et al., 2013), it is not necessarily a precise or reliable measure of the true seed size because it may depend on the shape (Alonso-Blanco et al., 1999) and the orientation of a seed at scan (see “Results”). These issues also apply when using 2D projections to calculate length-to-width ratios as a simple shape factor (Tanabata et al., 2012). Projected seed area also has been used to calculate seed mass, assuming a fixed relationship between these parameters (de Jong et al., 2011; Herridge et al., 2011). This may hold with sufficient accuracy when averaging a large number of seeds but might be misleading when considering individual seeds.From a physical point of view, volume should be a much better proxy for mass than 2D traits. Although it has been stated that for 65 species analyzed seed masses can be compared easily with seed volumes (Moles et al., 2005), it is not clear how these seed volumes were determined. Volumes can be assessed using advanced methods such as x-ray computed tomography (CT) on fruits (Stuppy et al., 2003) or synchrotron radiation x-ray tomographic microscopy applied in paleobiological studies (e.g. on fruits and seed; Friis et al., 2014). Nuclear magnetic resonance (NMR) methods are used to measure water uptake in kidney beans (Phaseolus vulgaris) and adzuki beans (Vigna angularis; Kikuchi et al., 2006) or to estimate seed weight and content (Borisjuk et al., 2011; Rolletschek et al., 2015) rather than volumes. To our best knowledge, affordable methods to measure seed volumes directly are not achievable so far. For that reason, we have set up a volume-carving method for 3D seed shape reconstruction that is described briefly here and in more detail in a recent publication (Roussel et al., 2016).While traits derived from scanning procedures can easily be assigned to individual seeds (Herridge et al., 2011), further handling and processing of phenotyped single seeds is not as simple, in particular for tiny ones like those of Arabidopsis. The aim of this work was to develop an automated seed-handling system that can analyze single seeds of very different sizes or shapes, from Arabidopsis seeds up to barley (Hordeum vulgare) seeds or even bigger. The phenoSeeder system is designed to pick and place seeds, to achieve basic morphometric traits (one-dimensional and 2D data from projections, 3D reconstruction data, and mass) of each individual seed, and to store all analyzed seed traits in a database. Another goal is to use phenoSeeder for seed-to-plant tracking approaches and to analyze whether, or which, particular seed traits have an impact on plant development and performance under various environmental conditions. We describe the main features of the phenoSeeder technology and present results obtained with seeds of three accessions of Arabidopsis, rapeseed (Brassica napus), and barley, respectively. When analyzing the data, we focused particularly on correlations between projected seed area, seed volume, and seed mass, with the hypothesis that the respective seed volume may better correlate with mass than the projected area.