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

Abstract

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 roundshaped 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.

Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping

“…New methods use various optical sensing techniques to estimate seed quality and safety (Huang et al, 2015), describe complex seed shapes using 2D images (Williams et al, 2013; Cervantes et al, 2016). Breakthrough 3D imaging technology and robotics (Jahnke et al, 2016; Roussel et al, 2016) or X-ray computed tomography (Strange et al, 2015) implemented for evaluating seed shape in fine detail. However, there is still a need for seed phenotyping using simple and low cost tools (Whan et al, 2014).…”

“…They used 3D surface reconstruction from the silhouettes of several images obtained by rotation of a seed in front of a digital camera. This method was implemented further in the pheno Seeder robotic platform (Jahnke et al, 2016), which was designed for the high-quality measurement of basic seed biometric traits and mass from which seed density is calculated. Strange et al (2015) used X-ray computed tomography for the in situ determination of grain shape.…”

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

“…rhizosheath phenotyping because nodal roots grew after seminal roots were cut for weighing. Noninvasive phenotyping technologies not only measure multiple traits in parallel (discussed above), they do so without destroying the plant over time, keeping seed viability [49]. Alleles of multiple desirable traits are advanced specifically in the seed expressing the traits ( Figure 3B).…”

Raspberry Pi Powered Imaging for Plant Phenotyping

“…Here, we document three different methods for using Raspberry Pi computers for plant phenotyping (Figure 1.). These protocols are a valuable resource because while there are many phenotyping papers that outline phenotyping systems in detail (Granier et al, 2006; Iyer-Pascuzzi et al, 2010; Jahnke et al, 2016; Shafiekhani et al, 2017), there are few protocols that provide step-by-step instructions for building them (Bodner et al, 2017; Minervini et al, 2017). We provide examples illustrating automation of photo capture with open source tools (based on Python and standard Linux utilities).…”