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

Regulation of root growth and elongation in wheat

Currently, the control of rhizosphere selection on farms has been applied to achieve enhancements in phenotype, extending from improvements in single root characteristics to the dynamic nature of entire crop systems. Several specific signals, regulatory elements, and mechanisms that regulate the initiation, morphogenesis, and growth of new lateral or adventitious root species have been identified, but much more work remains. Today, phenotyping technology drives the development of root traits. Available models for simulation can support all phenotyping decisions (root trait improvement). The detection and use of markers for quantitative trait loci (QTLs) are effective for enhancing selection efficiency and increasing reproductive genetic gains. Furthermore, QTLs may help wheat breeders select the appropriate roots for efficient nutrient acquisition. Single-nucleotide polymorphisms (SNPs) or alignment of sequences can only be helpful when they are associated with phenotypic variation for root development and elongation. Here, we focus on major root development processes and detail important new insights recently generated regarding the wheat genome. The first part of this review paper discusses the root morphology, apical meristem, transcriptional control, auxin distribution, phenotyping of the root system, and simulation models. In the second part, the molecular genetics of the wheat root system, SNPs, TFs, and QTLs related to root development as well as genome editing (GE) techniques for the improvement of root traits in wheat are discussed. Finally, we address the effect of omics strategies on root biomass production and summarize existing knowledge of the main molecular mechanisms involved in wheat root development and elongation.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Digital techniques and trends for seed phenotyping using optical sensors

BACKGROUND

The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency.

AIM OF REVIEW

This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.

YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor

Introduction

Drought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.

Methods

We constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.

Results

The experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.

Discussion

Our model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.

Pitfalls and potential of high-throughput plant phenotyping platforms

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

Arabidopsis histone deacetylase HD2A and HD2B regulate seed dormancy by repressing DELAY OF GERMINATION 1

Seed dormancy is a crucial developmental transition that affects the adaption and survival of plants. Arabidopsis DELAY OF GERMINATION 1 (DOG1) is known as a master regulator of seed dormancy. However, although several upstream factors of DOG1 have been reported, the exact regulation of DOG1 is not fully understood. Histone acetylation is an important regulatory layer, controlled by histone acetyltransferases and histone deacetylases. Histone acetylation strongly correlates with transcriptionally active chromatin, whereas heterochromatin is generally characterized by hypoacetylated histones. Here we describe that loss of function of two plant-specific histone deacetylases, HD2A and HD2B, resulted in enhanced seed dormancy in Arabidopsis. Interestingly, the silencing of HD2A and HD2B caused hyperacetylation of the DOG1 locus and promoted the expression of DOG1 during seed maturation and imbibition. Knockout of DOG1 could rescue the seed dormancy and partly rescue the disturbed development phenotype of hd2ahd2b. Transcriptomic analysis of the hd2ahd2b line shows that many genes involved in seed development were impaired. Moreover, we demonstrated that HSI2 and HSL1 interact with HD2A and HD2B. In sum, these results suggest that HSI2 and HSL1 might recruit HD2A and HD2B to DOG1 to negatively regulate DOG1 expression and to reduce seed dormancy, consequently, affecting seed development during seed maturation and promoting seed germination during imbibition.

Molecular dynamics of seed priming at the crossroads between basic and applied research

The potential of seed priming is still not fully exploited. Our limited knowledge of the molecular dynamics of seed pre-germinative metabolism is the main hindrance to more effective new-generation techniques. Climate change and other recent global crises are disrupting food security. To cope with the current demand for increased food, feed, and biofuel production, while preserving sustainability, continuous technological innovation should be provided to the agri-food sector. Seed priming, a pre-sowing technique used to increase seed vigor, has become a valuable tool due to its potential to enhance germination and stress resilience under changing environments. Successful priming protocols result from the ability to properly act on the seed pre-germinative metabolism and stimulate events that are crucial for seed quality. However, the technique still requires constant optimization, and researchers are committed to addressing some key open questions to overcome such drawbacks. In this review, an update of the current scientific and technical knowledge related to seed priming is provided. The rehydration-dehydration cycle associated with priming treatments can be described in terms of metabolic pathways that are triggered, modulated, or turned off, depending on the seed physiological stage. Understanding the ways seed priming affects, either positively or negatively, such metabolic pathways and impacts gene expression and protein/metabolite accumulation/depletion represents an essential step toward the identification of novel seed quality hallmarks. The need to expand the basic knowledge on the molecular mechanisms ruling the seed response to priming is underlined along with the strong potential of applied research on primed seeds as a source of seed quality hallmarks. This route will hasten the implementation of seed priming techniques needed to support sustainable agriculture systems.

WSVAS: A YOLOv4 -based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour

Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate (Recall) of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding.

Seed size, endosperm and germination variation in sexual and apomictic Boechera

Asexual reproduction results in offspring that are genetically identical to the mother. Among apomictic plants (reproducing asexually through seeds) many require paternal genetic contribution for proper endosperm development (pseudogamous endosperm). We examined phenotypic diversity in seed traits using a diverse panel of sexual and apomictic accessions from the genus Boechera. While genetic uniformity resulting from asexual reproduction is expected to reduce phenotypic diversity in seeds produced by apomictic individuals, pseudogamous endosperm, variable endosperm ploidy, and the deviations from 2:1 maternal:paternal genome ratio in endosperm can all contribute to increased phenotypic diversity among apomictic offspring. We characterized seed size variation in 64 diploid sexual and apomictic (diploid and triploid) Boechera lineages. In order to find out whether individual seed size was related to endosperm ploidy we performed individual seed measurements (projected area and mass) using the phenoSeeder robot system and flow cytometric seed screen. In order to test whether individual seed size had an effect on resulting fitness we performed a controlled growth experiment and recorded seedling life history traits (germination success, germination timing, and root growth rate). Seeds with triploid embryos were 33% larger than those with diploid embryos, but no average size difference was found between sexual and apomictic groups. We identified a maternal effect whereby chloroplast lineage 2 had 30% larger seeds than lineage 3, despite having broad and mostly overlapping geographic ranges. Apomictic seeds were not more uniform in size than sexual seeds, despite genetic uniformity of the maternal gametophyte in the former. Among specific embryo/endosperm ploidy combinations, seeds with tetraploid (automomous) endosperm were on average smaller, and the proportion of such seeds was highest in apomicts. Larger seeds germinated more quickly than small seeds, and lead to higher rates of root growth in young seedlings. Seed mass is under balancing selection in Boechera, and it is an important predictor of several traits, including germination probability and timing, root growth rates, and developmental abnormalities in apomictic accessions.

High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r²  = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r²  = 0.9996) and highly accurate fruit counts (r²  = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

Empirical Evaluation of Inflorescences' Morphological Attributes for Yield Optimization of Medicinal Cannabis Cultivars

In recent decades with the reacknowledgment of the medicinal properties of Cannabis sativa L. (cannabis) plants, there is an increased demand for high performing cultivars that can deliver quality products for various applications. However, scientific knowledge that can facilitate the generation of advanced cannabis cultivars is scarce. In order to improve cannabis breeding and optimize cultivation techniques, the current study aimed to examine the morphological attributes of cannabis inflorescences using novel image analysis practices. The investigated plant population comprises 478 plants ascribed to 119 genotypes of high-THC or blended THC-CBD ratio that was cultivated under a controlled environment facility. Following harvest, all plants were manually processed and an image of the trimmed and refined inflorescences extracted from each plant was captured. Image analysis was then performed using in-house custom-made software which extracted 8 morphological features (such as size, shape and perimeter) for each of the 127,000 extracted inflorescences. Our findings suggest that environmental factors play an important role in the determination of inflorescences' morphology. Therefore, further studies that focus on genotype X environment interactions are required in order to generate inflorescences with desired characteristics. An examination of the intra-plant inflorescences weight distribution revealed that processing 75% of the plant's largest inflorescences will gain 90% of its overall yield weight. Therefore, for the optimization of post-harvest tasks, it is suggested to evaluate if the benefits from extracting and processing the plant's smaller inflorescences outweigh its operational costs. To advance selection efficacy for breeding purposes, a prediction equation for forecasting the plant's production biomass through width measurements of specific inflorescences, formed under the current experimental methodology, was generated. Thus, it is anticipated that findings from the current study will contribute to the field of medicinal cannabis by improving targeted breeding programs, advancing crop productivity and enhancing the efficacy of post-harvest procedures.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

High-Throughput Extraction of Seed Traits Using Image Acquisition and Analysis

Seed traits can easily be assessed using image processing tools to evaluate differences in crop variety performances in response to environment and stress. In this chapter, we describe a protocol to measure seed traits that can be applied to crops with small grains, including legume grains with little modification. The imaging processing tool can be applied to process a batch of images without human intervention. The method allows evaluation of geometric and color features, and currently extracts 11 seed traits that include number of seeds, seed area, major axis, minor axis, eccentricity, and mean and standard deviation of reflectance in red, green, and blue channels from seed images. Protocols or methods, including the one described in this chapter, facilitate phenotyping seed traits in a high-throughput and automated manner, which can be applied in plant breeding programs and food processing industry to evaluate seed quality.

Current status and future challenges in implementing and upscaling vertical farming systems

Vertical farming can produce food in a climate-resilient manner, potentially emitting zero pesticides and fertilizers, and with lower land and water use than conventional agriculture. Vertical farming systems (VFS) can meet daily consumer demands for nutritious fresh products, forming a part of resilient food systems-particularly in and around densely populated areas. VFS currently produce a limited range of crops including fruits, vegetables and herbs, but successful implementation of vertical farming as part of mainstream agriculture will require improvements in profitability, energy efficiency, public policy and consumer acceptance. Here we discuss VFS as multi-layer indoor crop cultivation systems, exploring state-of-the-art vertical farming and future challenges in the fields of plant growth, product quality, automation, robotics, system control and environmental sustainability and how research and development, socio-economic and policy-related institutions must work together to ensure successful upscaling of VFS to future food systems.

Grading of Scots Pine Seeds by the Seed Coat Color: How to Optimize the Engineering Parameters of the Mobile Optoelectronic Device

Research Highlights: There is a problem of forest seeds quality assessment and grading afield in minimal costs. The grading quality of each seed coat color class is determined by the degree of its separation with a mobile optoelectronic grader. Background and Objectives: Traditionally, pine seeds are graded in size, but this can lead to a loss of genetic diversity. Seed coat color is individual for each forest seed and is caused to a low error in identifying the genetic features of seedling obtained from it. The principle on which the mobile optoelectronic grader operates is based on the optical signal detection reflected from the single seed. The grader can operate in scientific (spectral band analysis) mode and production (spectral feature grading) mode. When operating in production mode, it is important to determine the optimal engineering parameters of the grader that provide the maximum value of the separation degree of seed-color classes. For this purpose, a run of experiments was conducted on the forest seeds separation using a mobile optoelectronic grader and regression models of the output from factors were obtained. Materials and Methods: Scots pine (Pinus sylvestris L.) seed samples were obtained from cones of the 2019 harvest collected in a natural stand. The study is based on the Design of Experiments theory (DOE) using the Microsoft Excel platform. In each of three replications of each run from the experiment matrix, a mixture of 100 seeds of light, dark and light-dark fraction (n = 300) was used. Results: Interpretation of the obtained regression model of seed separation in the visible wavelength range (650–715 nm) shows that the maximum influence on the output—separation degree—is exerted by the angle of incidence of the detecting optical beam. Next in terms of the influence power on the output are paired interactions: combinations of the wavelength with the angle of incidence and the wavelength with the grader’s seed pipe height. The minimum effect on the output is the wavelength of the detecting optical beam. Conclusions: The use of a mobile optoelectronic grader will eliminate the cost of transporting seeds to and from forest seed centers. To achieve a value of 0.97–1.0 separation degree of Scots pine seeds colored fractions, it is necessary to provide the following optimal engineering parameters of the mobile optoelectronic grader: the wavelength of optical radiation is 700 nm, the angle of incidence of the detecting optical beam is 45° and the grader’s seed pipe height is 0.2 m.

On the verge of the market - Plant factories for the automated and standardized production of biopharmaceuticals

The market for biopharmaceuticals is dominated by recombinant proteins and is driven mainly by the development of vaccines and antibodies. Manufacturing predominantly relies on fermentation-based production platforms, which have limited scalability and suffer from high upstream process costs. As an alternative, the production of recombinant proteins in whole plants (plant molecular farming) provides a scalable and cost efficient upstream process because each plant functions as a self-contained bioreactor, avoiding costs associated with single-use devices and cleaning-in-place. Despite many proof-of-concept studies and the approval of a few products as medical devices, the only approved pharmaceutical proteins manufactured in whole plants have been authorized under emergency protocols. The absence of approvals under standard clinical development pathways in part reflects the lack of standardized process equipment and unit operations, leading to industry inertia based on familiarity with fermenter systems. Here we discuss the upstream production steps of plant molecular farming by transient expression in intact plants, including seeding, plant cultivation, infiltration with Agrobacterium tumefaciens, post-infiltration incubation, and harvesting. We focus on cultivation techniques because they strongly affect the subsequent steps and overall process design. We compare the benefits and drawbacks of open field, greenhouse and vertical farm strategies in terms of upfront investment costs, batch reproducibility, and decoupling from environmental impacts. We consider process automation, monitoring and adaptive process design in the context of Industry 4.0, which can boost process efficiency and batch-to-batch uniformity to improve regulatory compliance. Finally, we discuss the costs-benefit aspects of the different cultivation systems.

Species-independent analytical tools for next-generation agriculture

Innovative approaches are urgently required to alleviate the growing pressure on agriculture to meet the rising demand for food. A key challenge for plant biology is to bridge the notable knowledge gap between our detailed understanding of model plants grown under laboratory conditions and the agriculturally important crops cultivated in fields or production facilities. This Perspective highlights the recent development of new analytical tools that are rapid and non-destructive and provide tissue-, cell- or organelle-specific information on living plants in real time, with the potential to extend across multiple species in field applications. We evaluate the utility of engineered plant nanosensors and portable Raman spectroscopy to detect biotic and abiotic stresses, monitor plant hormonal signalling as well as characterize the soil, phytobiome and crop health in a non- or minimally invasive manner. We propose leveraging these tools to bridge the aforementioned fundamental gap with new synthesis and integration of expertise from plant biology, engineering and data science. Lastly, we assess the economic potential and discuss implementation strategies that will ensure the acceptance and successful integration of these modern tools in future farming practices in traditional as well as urban agriculture.

SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination

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.

Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets

Background

The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.

Results

The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results.

Conclusion

The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.

High-throughput phenotyping of brachiaria grass seeds using free access tool for analyzing X-ray images

New approaches based on image analysis can assist in phenotyping of biological characteristics, serving as support for decision-making in modern agribusiness. The aim of this study was to propose a method of high-throughput phenotyping of free access for processing of 2D X-ray images of brachiaria grass (Brachiaria ruziziensis cv. Ruziziensis) seeds, as well as correlate the parameters linked to the physiological potential of the seeds. The study was carried out by means of automated analysis of X-ray images of seeds in which a macro, called PhenoXray, was developed, responsible for digital image processing, for which a series of descriptors were obtained. After the X-ray analysis, a germination test was performed on the seeds and, from this, variables related to the physiological quality of the seeds were obtained. The use of the macro PhenoXray allowed large-scale phenotyping of seed X-rays in a simple, rapid, robust, and totally free manner. This study confirmed that the methodology is efficient for obtaining morphometric data and tissue integrity data in Brachiaria ruziziensis seeds and that parameters such as relative density, integrated density, and seed filling are closely related to the physiological attributes of seed quality.

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.

Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

Phenotyping: New Windows into the Plant for Breeders

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

A high throughput method for quantifying number and size distribution of Arabidopsis seeds using large particle flow cytometry

BACKGROUND

Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as Arabidopsis thaliana, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods.

RESULTS

We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual seeds and sort them according to size, allowing an average of 12,000 seeds/hour to be processed. To achieve this high throughput, post harvested seeds are first separated from remaining plant material (dust and chaff) using a rapid sedimentation-based method. Then, classification algorithms are used to refine the separation process in silico. Accurate identification of all seeds in the samples was achieved, with relative errors below 2%.

CONCLUSION

The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Precise Volumetric Measurements of Any Shaped Objects with a Novel Acoustic Volumeter

We introduce a novel technique to measure volumes of any shaped objects based on acoustic components. The focus is on small objects with rough surfaces, such as plant seeds. The method allows measurement of object volumes more than 1000 times smaller than the volume of the sensor chamber with both high precision and high accuracy. The method is fast, noninvasive, and easy to produce and use. The measurement principle is supported by theory, describing the behavior of the measured data for objects of known volumes in a range of 1 to 800 µL. In addition to single-frequency, we present frequency-dependent measurements that provide supplementary information about pores on the surface of a measured object, such as the total volume of pores and, in the case of cylindrical pores, their average radius-to-length ratio. We demonstrate the usefulness of the method for seed phenotyping by measuring the volume of irregularly shaped seeds and showing the ability to "look" under the husk and inside pores, which allows us to assess the true density of seeds.

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today's phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.

Measuring crops in 3D: using geometry for plant phenotyping

Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.

From zero to hero: the past, present and future of grain amaranth breeding

Grain amaranth is an underutilized crop with high nutritional quality from the Americas. Emerging genomic and biotechnological tools are becoming available that allow the integration of novel breeding techniques for rapid improvement of amaranth and other underutilized crops. Out of thousands of edible plants, only three cereals-maize, wheat and rice-are the major food sources for a majority of people worldwide. While these crops provide high amounts of calories, they are low in protein and other essential nutrients. The dependence on only few crops, with often narrow genetic basis, leads to a high vulnerability of modern cropping systems to the predicted climate change and accompanying weather extremes. Broadening our food sources through the integration of so-called orphan crops can help to mitigate the effects of environmental change and improve qualitative food security. Thousands of traditional crops are known, but have received little attention in the last century and breeding efforts were limited. Amaranth is such an underutilized pseudocereal that is of particular interest because of its balanced amino acid and micronutrient profiles. Additionally, the C₄ photosynthetic pathway and ability to withstand environmental stress make the crop a suitable choice for future agricultural systems. Despite the potential of amaranth, efforts of genetic improvement lag considerably behind those of major crops. The progress in novel breeding methods and molecular techniques developed in model plants and major crops allow a rapid improvement of underutilized crops. Here, we review the history of amaranth and recent advances in genomic tools and give a concrete perspective how novel breeding techniques can be implemented into breeding programs. Our perspectives are transferable to many underutilized crops. The implementation of these could improve the nutritional quality and climate resilience of future cropping systems.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Raspberry Pi-powered imaging for plant phenotyping

PREMISE OF THE STUDY

Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.

METHODS AND RESULTS

We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV.

CONCLUSIONS

This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

Raspberry Pi-powered imaging for plant phenotyping

PREMISE OF THE STUDY

Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.

METHODS AND RESULTS

We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV.

CONCLUSIONS

This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

BACKGROUND

In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.

RESULTS

We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.

CONCLUSIONS

The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping

Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC. Furthermore, the effectiveness of analysis performed in the field can be improved through the use of mobile devices. In this paper, we propose a method for the automated evaluation of phenotypic parameters of grains using mobile devices running the Android operational system. The experimental results show that this approach is efficient and sufficiently accurate for the large-scale analysis of phenotypic characteristics in wheat grains. Evaluation of our application under six different lighting conditions and three mobile devices demonstrated that the lighting of the paper has significant influence on the accuracy of our method, unlike the smartphone type.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.