Abstract
Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.
Highlights
Seeds are essential for human beings, as important food sources, and for efficient crop production
Analysis outputs include two types of traits: (1) germination traits quantified using 1st~186th images (Fig. 4b), including cumulative germination curves, T50 germination rates to assess the uniformity of germination, and Gmax to quantify the proportion of seeds germinated at the end of the experiment; and, (2) morphological traits quantified using 1st~160th images (Fig. 4c), including seed area, width and length (W/L) ratio, and circularity
We present the SeedGerm system that integrates cost-effective hardware and user-friendly software for performing seed imaging and ML-based analysis for measuring establishment- and germination-related traits
Summary
Seeds are essential for human beings, as important food sources, and for efficient crop production. Routine germination scoring still commonly relies on human observation, which has practically constrained the frequency, scale, and accuracy of such experiments (Reyazul et al, 2015; Jahnke et al, 2016; Zhang et al, 2018) This bottleneck has led to many attempts to automate both seed imaging and associated phenotypic analysis, resulting in several research-based solutions such as GERMINATOR and the package, phenoSeeder, and the MultiSense tool (Ducournau et al, 2005; Joosen et al, 2010; Demilly et al, 2015; Jahnke et al, 2016; Ligterink & Hilhorst, 2016; Keil et al, 2017). Advanced computer-vision (CV) and machine-learning (ML) techniques are being applied to germination assays, including the Rice Seed Germination Evaluation System (RSGES) for assessing the germination status of Thai rice species using an artificial neural network (ANN) classifier (Lurstwut & Pornpanomchai, 2017); machine-vision based analysis on visible and X-ray images for evaluating soybean seed quality based on physical purity, viability and vigour (Mahajan et al, 2018); deep learning (DL) algorithms such as U-Net and ResNet for segmenting and classifying rice seed germination status (Nguyen et al, 2018); linear discriminant analysis and multispectral imaging combined for classifying cowpea seeds into categories of ageing, germination, and normality (Elmasry et al, 2019), and a high-throughput micro-CT-RGB (HCR) phenotyping system for dissecting the rice genetic architecture from seedling (Wu et al, 2019)
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