Image-based high-throughput field phenotyping of crop roots

Silicon and selenium alleviate cadmium toxicity in Artemisia selengensis Turcz by regulating the plant-rhizosphere

Soil cadmium (Cd) pollution has emerged as a pressing concern due to its deleterious impacts on both plant physiology and human well-being. Silicon (Si) is renowned for its ability to mitigate excessive Cd accumulation within plant cells and reduce the mobility of Cd in soil, whereas Selenium (Se) augments plant antioxidant capabilities and promotes rhizosphere microbial activity. However, research focusing on the simultaneous utilization of Si and Se to ameliorate plant Cd toxicity through multiple mechanisms within the plant-rhizosphere remains comparatively limited. This study combined hydroponic and pot experiments to investigate the effects of the combined application of Si and Se on Cd absorption and accumulation, as well as the growth and rhizosphere of A. selengensis Turcz under Cd stress. The results revealed that a strong synergistic effect was observed between both Si and Se. The combination of Si and Se significantly increased the activity and content of enzymes and non-enzyme antioxidants within A. selengensis Turcz, reduced Cd accumulation and inhibiting its translocation from roots to shoots. Moreover, Si and Se application improved the levels of reducing sugar, soluble protein, and vitamin C, while reducing nitrite content and Cd bioavailability. Furthermore, the experimental results showed that the combination of Si and Se not only increased the abundance of core rhizosphere microorganisms, but also stimulated the activity of soil enzymes, which effectively limited the migration of Cd in the soil. These findings provided valuable insights into the effective mitigation of soil Cd toxicity to plants and also the potential applications in improving plant quality and safety.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection

Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.

Evaluation of functional kompetitive allele-specific PCR (KASP) markers for selection of drought-tolerant wheat (Triticum aestivum) genotypes

Wheat (Triticum aestivum ) is a major crop around the globe and different techniques are being used for its productivity enhancement. Germplasm evaluation to improve crop productivity mainly depends on accurate phenotyping and selection of genotypes with a high frequency of superior alleles related to the trait of interest. Therefore, applying functional kompetitive allele-specific PCR (KASP) markers for drought-related genes is essential to characterise the genotypes for developing future climate-resilient wheat crop. In this study, eight functional KASP markers and nine morphological traits were employed to evaluate the 40 wheat genotypes for drought tolerance. Morphological traits showed significant variation (P ≤0.05) among the genotypes, except tiller count (TC), fresh root weight (FRW) and dry root weight (DRW). PCA biplot showed that 63.3% phenotypic variation was explained by the first two PCs under control treatment, while 70.8% variation was explained under drought treatment. It also indicated that root length (RL) and primary root (PR) have considerable variations among the genotypes under both treatments and are positively associated with each other. Hence, the findings of this study suggested that both these traits could be used as a selection criterion to classify the drought-tolerant wheat genotypes. KASP genotyping accompanied by morphological data revealed that genotypes Markaz, Bhakar Star, China 2, Aas and Chakwal-50 performed better under drought stress. These outperforming genotypes could be used as parents in developing drought-tolerant wheat genotypes. Hence, KASP genotyping assay for functional genes or significant haplotypes and phenotypic evaluation are prerequisites for a modern breeding program.

Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models

BACKGROUND

Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset.

RESULTS

The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives.

CONCLUSIONS

Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining.

Phenotypic quantification of root spatial distribution along circumferential direction for field paddy-wheat

Plant roots are essential for water and nutrient absorption, anchoring, mechanical support, metabolite storage and interaction with the surrounding soil environment. A comprehensive understanding of root traits provides an opportunity to build ideal roots architectural system that provides improved stability and yield advantage in adverse target environments caused by soil quality degradation, climate change, etc. However, we hypothesize that quantitative indicators characterizing root system are still need to be supplemented. Features describing root growth and distribution, until now, belong mostly to 2D indicators or reflect changes in the root system with a depth of soil layers but are rarely considered in a spatial region along the circumferential direction. We proposed five new indicators to quantify the dynamics of the root system architecture (RSA) along its eight-part circumferential orientations with visualization technology which consists of in-situ field root samplings, RSA digitization, and reconstruction according to previous research based on field experiments that conducted on paddy-wheat cultivation land with three fertilization rates. The experimental results showed that the growth space of paddy-wheat root is mainly restricted to a cylinder with a diameter of 180 mm and height of 200 mm at the seedlings stage. There were slow fluctuating trends in growth by the mean values of five new indicators within a single volume of soil. The fluctuation of five new indicators was indicated in each sampling time, which decreased gradually with time. Furthermore, treatment of N70 and N130 could similarly impact root spatial heterogeneity. Therefore, we concluded that the five new indicators could quantify the spatial dynamics of the root system of paddy-wheat at the seedling stage of cultivation. It is of great significance to the comprehensive quantification of crop roots in targeted breeding programs and the methods innovation of field crop root research.

Quantification of the three-dimensional root system architecture using an automated rotating imaging system

BACKGROUND

Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time.

RESULTS

We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline.

CONCLUSIONS

The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant-Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.

Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation

This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Bacillus aryabhattai LAD impacts rhizosphere bacterial community structure and promotes maize plant growth

BACKGROUND

Plant growth-promoting rhizobacteria may significantly impact the soil microbial community and the growth of plant roots and have critical roles in soil ecosystem functioning. However, the interactions between rhizobacteria and plants are extremely complicated and remain understudied.

RESULTS

In this study, a Bacillus strain was isolated from a long-term maize colonization soil and identified as Bacillus aryabhattai strain LAD. Laboratory tests showed that B. aryabhattai LAD had phosphate-solubilizing and nitrogen-fixing functions that benefit plant growth. The effects of LAD cultures on the root system development of corn seedlings and the structure of rhizosphere bacterial communities were studied. The most significant stimulations of LAD culture on plant growth were observed at a cell density of 10²  CFU mL^-1 . Treatment with LAD culture in hydroponics caused an increase of 107%, 197%, and 25% in the shoot length, total root length, and main root thickness respectively. The LAD treatment also significantly affected the rhizosphere microbial abundance and community structure. The rhizobacterial abundance and species richness in the corn seedlings treated with LAD culture were significantly lower than those in the control group. However, the LAD-treated samples had higher relative abundances of plant growth-promoting rhizobacteria like Bacillus and Burkholderia than the control samples did, suggesting that LAD treatment may facilitate the mutualistic relation between the rhizosphere microbiome and the plant.

CONCLUSION

These results collectively demonstrated that LAD is capable of shaping the rhizosphere microbial community structure and functions as a plant growth-promoting agent, which makes it a strong candidate for application as bio-fertilizer in agricultural systems. © 2022 Society of Chemical Industry.

Assessing the Storage Root Development of Cassava with a New Analysis Tool

Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.

Molecular mechanisms of plant growth promotion for methylotrophic Bacillus aryabhattai LAD

Plant growth-promoting rhizobacteria (PGPR) can produce hormone-like substances, promote plant nutrient uptake, enhance plant resistance, inhibit the growth of pathogenic bacteria, and induce plant resistance to biotic and abiotic stresses. Bacillus is one of the most studied genera that promote plant root development. Since its discovery in 2009, B. aryabhattai has shown promising properties such as promoting plant growth and improving crop yield. However, the mechanisms of B. aryabhattai promoting plant growth remain to be investigated. In this study, the chromosome of B. aryabhattai strain LAD and five plasmids within the cell were sequenced and annotated. The genome, with a length of 5,194,589 bp and 38.12% GC content, contains 5,288 putative protein-coding genes, 39 rRNA, and 112 tRNA. The length of the five plasmids ranged from 116,519 to 212,484 bp, and a total of 810 putative protein-coding genes, 4 rRNA, and 32 tRNA were predicted in the plasmids. Functional annotation of the predicted genes revealed numerous genes associated with indole-3 acetic acid (IAA) and exopolysaccharides (EPSs) biosynthesis, membrane transport, nitrogen cycle metabolism, signal transduction, cell mobility, stress response, and antibiotic resistance on the genome which benefits the plants. Genes of carbohydrate-active enzymes were detected in both the genome and plasmids suggesting that LAD has the capacity of synthesizing saccharides and utilizing organic materials like root exudates. LAD can utilize different carbon sources of varied carbon chain length, i.e., methanol, acetate, glycerol, glucose, sucrose, and starch for growth and temperature adaptation suggesting a high versatility of LAD for thriving in fluctuating environments. LAD produced the most EPSs with sucrose as sole carbon source, and high concentration of IAA was produced when the maize plant was cultivated with LAD, which may enhance plant growth. LAD significantly stimulated the development of the maize root. The genome-based information and experimental evidence demonstrated that LAD with diverse metabolic capabilities and positive interactions with plants has tremendous potential for adaptation to the dynamic soil environments and promoting plant growth.

Response of Southeast Asian rice root architecture and anatomy phenotypes to drought stress

Drought stress in Southeast Asia greatly affects rice production, and the rice root system plays a substantial role in avoiding drought stress. In this study, we examined the phenotypic and genetic correlations among root anatomical, morphological, and agronomic phenotypes over multiple field seasons. A set of >200 rice accessions from Southeast Asia (a subset of the 3000 Rice Genomes Project) was characterized with the aim to identify root morphological and anatomical phenotypes related to productivity under drought stress. Drought stress resulted in slight increases in the basal metaxylem and stele diameter of nodal roots. Although few direct correlations between root phenotypes and grain yield were identified, biomass was consistently positively correlated with crown root number and negatively correlated with stele diameter. The accessions with highest grain yield were characterized by higher crown root numbers and median metaxylem diameter and smaller stele diameter. Genome-wide association study (GWAS) revealed 162 and 210 significant SNPs associated with root phenotypes in the two seasons which resulted in identification of 59 candidate genes related to root development. The gene OsRSL3 was found in a QTL region for median metaxylem diameter. Four SNPs in OsRSL3 were found that caused amino acid changes and significantly associated with the root phenotype. Based on the haplotype analysis for median metaxylem diameter, the rice accessions studied were classified into five allele combinations in order to identify the most favorable haplotypes. The candidate genes and favorable haplotypes provide information useful for the genetic improvement of root phenotypes under drought stress.

Leaf Count Aided Novel Framework for Rice (Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications

Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant-aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86-92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves' emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision.

Recent advances in methods for in situ root phenotyping

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization

The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.

Drought tolerance assessment of citron watermelon (Citrullus lanatus var. citroides (L.H. Bailey) Mansf. ex Greb.) accessions based on morphological and physiological traits

Long-term cultivation of citron watermelon under water-constrained environments in sub-Saharan Africa resulted in the selection and domestication of highly tolerant genotypes. However, information on the magnitude of variation for drought tolerance in citron watermelon is limited for the effective selection of suitable genotypes for breeding. The objective of this study was to determine variation for drought tolerance among South African citron watermelon landrace accessions for selection and use as genetic stock for drought-tolerance breeding in this crop and closely-related cucurbit crops. Forty genetically differentiated citron watermelon accessions were grown under non-stress (NS) and drought-stress (DS) conditions under glasshouse environment. Data of physiological (i.e., leaf gas exchange and chlorophyll fluorescence parameters) and morphological traits (i.e., shoot and root system architecture traits, and fruit yield) were collected and subjected to various parametric statistical analyses. The accessions varied significantly for assessed traits under both NS and DS conditions which aided classification into five groups, namely; A (highly drought-tolerant), B (drought-tolerant), C (moderate drought-tolerant), D (drought-sensitive) and E (highly drought-sensitive). Drought-tolerant genotypes produced more fruit yield with less water compared with drought-sensitive genotypes. Several physiological and morphological parameters correlated with fruit yield under DS condition namely: instantaneous water-use efficiency (r = 0.97), leaf dry weight (r = 0.77), total root length (r = 0.46) and root dry weight (r = 0.48). The following accessions, namely: WWM-46, WWM-68, WWM-41(A), WWM-15, WWM-64, WWM-57, WWM-47, WWM-37(2), WWM-79, WWM-05 and WWM-50) were identified as highly drought-tolerant and recommended for drought-tolerance breeding in this crop or related cucurbit crops such as sweet dessert watermelon.

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15)

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems

Rice (Oryza sativa) was domesticated around 10,000 years ago and has developed into a staple for half of humanity. The crop evolved and is currently grown in stably wet and intermittently dry agro-ecosystems, but patterns of adaptation to differences in water availability remain poorly understood. While previous field studies have evaluated plant developmental adaptations to water deficit, adaptive variation in functional and hydraulic components, particularly in relation to gene expression, has received less attention. Here, we take an evolutionary systems biology approach to characterize adaptive drought resistance traits across roots and shoots. We find that rice harbors heritable variation in molecular, physiological, and morphological traits that is linked to higher fitness under drought. We identify modules of co-expressed genes that are associated with adaptive drought avoidance and tolerance mechanisms. These expression modules showed evidence of polygenic adaptation in rice subgroups harboring accessions that evolved in drought-prone agro-ecosystems. Fitness-linked expression patterns allowed us to identify the drought-adaptive nature of optimizing photosynthesis and interactions with arbuscular mycorrhizal fungi. Taken together, our study provides an unprecedented, integrative view of rice adaptation to water-limited field conditions.

Harnessing root architecture to address global challenges

Root architecture can be targeted in breeding programs to develop crops with better capture of water and nutrients. In rich nations, such crops would reduce production costs and environmental pollution and, in developing nations, they would improve food security and economic development. Crops with deeper roots would have better climate resilience while also sequestering atmospheric CO₂ . Deeper rooting, which improves water and N capture, is facilitated by steeper root growth angles, fewer axial roots, reduced lateral branching, and anatomical phenotypes that reduce the metabolic cost of root tissue. Mechanical impedance, hypoxia, and Al toxicity are constraints to subsoil exploration. To improve topsoil foraging for P, K, and other shallow resources, shallower root growth angles, more axial roots, and greater lateral branching are beneficial, as are metabolically cheap roots. In high-input systems, parsimonious root phenotypes that focus on water capture may be advantageous. The growing prevalence of Conservation Agriculture is shifting the mechanical impedance characteristics of cultivated soils in ways that may favor plastic root phenotypes capable of exploiting low resistance pathways to the subsoil. Root ideotypes for many low-input systems would not be optimized for any one function, but would be resilient against an array of biotic and abiotic challenges. Root hairs, reduced metabolic cost, and developmental regulation of plasticity may be useful in all environments. The fitness landscape of integrated root phenotypes is large and complex, and hence will benefit from in silico tools. Understanding and harnessing root architecture for crop improvement is a transdisciplinary opportunity to address global challenges.

Phenotyping seedlings for selection of root system architecture in alfalfa (Medicago sativa L.)

BACKGROUND

The root system architecture (RSA) of alfalfa (Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software.

RESULTS

The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively.

CONCLUSIONS

The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle.

Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica

Two sorghum varieties, Shanqui Red (SQR) and SRN39, have distinct levels of susceptibility to the parasitic weed Striga hermonthica, which have been attributed to different strigolactone composition within their root exudates. Root exudates of the Striga-susceptible variety Shanqui Red (SQR) contain primarily 5-deoxystrigol, which has a high efficiency for inducing Striga germination. SRN39 roots primarily exude orobanchol, leading to reduced Striga germination and making this variety resistant to Striga. The structural diversity in exuded strigolactones is determined by a polymorphism in the LOW GERMINATION STIMULANT 1 (LGS1) locus. Yet, the genetic diversity between SQR and SRN39 is broad and has not been addressed in terms of growth and development. Here, we demonstrate additional differences between SQR and SRN39 by phenotypic and molecular characterization. A suite of genes related to metabolism was differentially expressed between SQR and SRN39. Increased levels of gibberellin precursors in SRN39 were accompanied by slower growth rate and developmental delay and we observed an overall increased SRN39 biomass. The slow-down in growth and differences in transcriptome profiles of SRN39 were strongly associated with plant age. Additionally, enhanced lateral root growth was observed in SRN39 and three additional genotypes exuding primarily orobanchol. In summary, we demonstrate that the differences between SQR and SRN39 reach further than the changes in strigolactone profile in the root exudate and translate into alterations in growth and development.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

BACKGROUND

Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA).

RESULTS

Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R²) of 0.84 and a P < 0.001.

CONCLUSIONS

The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Growth dynamics and heritability for plant high-throughput phenotyping studies using hierarchical functional data analysis

In modern high-throughput plant phenotyping, images of plants of different genotypes are repeatedly taken throughout the growing season, and phenotypic traits of plants (e.g., plant height) are extracted through image processing. It is of interest to recover whole trait trajectories and their derivatives at both genotype and plant levels based on observations made at irregular discrete time points. We propose to model trait trajectories using hierarchical functional principal component analysis (HFPCA) and show that the problem of recovering derivatives of the trajectories is reduced to estimating derivatives of eigenfunctions, which is solved by differentiating eigenequations. Based on HFPCA, we also propose a new measure for the broad-sense heritability by allowing it to vary over time during plant growth. Simulation studies show that the proposed procedure performs better than its competitors in terms of recovering both trait trajectories and their derivatives. Interesting characteristics of plant growth and heritability dynamics are revealed in the application to a modern plant phenotyping study.

Root-omics for drought tolerance in cool-season grain legumes

Root traits can be exploited to increase the physiological efficiency of crop water use under drought. Root length, root hairs, root branching, root diameter, and root proliferation rate are genetically defined traits that can help to improve the water productivity potential of crops. Recently, high-throughput phenotyping techniques/platforms have been used to screen the germplasm of major cool-season grain legumes for root traits and their impact on different physiological processes, including nutrient uptake and yield potential. Advances in omics approaches have led to the dissection of genomic, proteomic, and metabolomic structures of these traits. This knowledge facilitates breeders to improve the water productivity and nutrient uptake of cultivars under limited soil moisture conditions in major cool-season grain legumes that usually face terminal drought. This review discusses the advances in root traits and their potential for developing drought-tolerant cultivars in cool-season grain legumes.

Addressing Research Bottlenecks to Crop Productivity

Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source-sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies.

Root System Architecture Plasticity of Bread Wheat in Response to Oxidative Burst under Extended Osmotic Stress

There is a demand for an increase in crop production because of the growing population, but water shortage hinders the expansion of wheat cultivation, one of the most important crops worldwide. Polyethylene glycol (PEG) was used to mimic drought stress due to its high osmotic potentials generated in plants subjected to it. This study aimed to determine the root system architecture (RSA) plasticity of eight bread wheat genotypes under osmotic stress in relation to the oxidative status and mitochondrial membrane potential of their root tips. Osmotic stress application resulted in differences in the RSA between the eight genotypes, where genotypes were divided into adapted genotypes that have non-significant decreased values in lateral roots number (LRN) and total root length (TRL), while non-adapted genotypes have a significant decrease in LRN, TRL, root volume (RV), and root surface area (SA). Accumulation of intracellular ROS formation in root tips and elongation zone was observed in the non-adapted genotypes due to PEG-induced oxidative stress. Mitochondrial membrane potential (∆Ψm) was measured for both stress and non-stress treatments in the eight genotypes as a biomarker for programmed cell death as a result of induced osmotic stress, in correlation with RSA traits. PEG treatment increased scavenging capacity of the genotypes from 1.4-fold in the sensitive genotype Gemmiza 7 to 14.3-fold in the adapted genotype Sakha 94. The adapted genotypes showed greater root trait values, ∆Ψm plasticity correlated with high scavenging capacity, and less ROS accumulation in the root tissue, while the non-adapted genotypes showed little scavenging capacity in both treatments, accompanied by mitochondrial membrane permeability, suggesting mitochondrial dysfunction as a result of oxidative stress.

A multiple ion-uptake phenotyping platform reveals shared mechanisms affecting nutrient uptake by roots

Nutrient uptake is critical for crop growth and is determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively little is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.). Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate, and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed substantial genetic variation for multiple ion-uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion-uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition.

Soil Water Deficit and Fertilizer Placement Effects on Root Biomass Distribution, Soil Water Extraction, Water Use, Yield, and Yield Components of Soybean [Glycine max (L.) Merr.] Grown in 1-m Rooting Columns

Typical small-pot culture systems are not ideal for controlled environment phenotyping for drought tolerance, especially for root-related traits. We grew soybean plants in a greenhouse in 1-m rooting columns filled with amended field soil to test the effects of drought stress on water use, root growth, shoot growth, and yield components. There were three watering treatments, beginning at first flower: watered daily to 100% of the maximum soil water holding capacity (control), 75% (mild drought stress), or 50% (drought stress). We also tested whether applying fertilizer throughout the 1-m soil depth instead of only in the top 30 cm would modify root distribution by depth in the soil profile and thereby affect responses to drought stress. Distributing the fertilizer over the entire 1-m soil depth altered the root biomass distribution and volumetric soil water content profile at first flower, but these effects did not persist to maturity and thus did not enhance drought tolerance. Compared to the control (100%) watering treatment, the 50% watering treatment significantly reduced seed yield by 40%, pod number by 42%, seeds per pod by 3%, shoot dry matter by 48%, root dry matter by 53%, and water use by 52%. Effects of the 75% watering treatment were intermittent between the 50 and 100%. The 50% treatment significantly increased root-to-shoot dry matter ratio by 23%, harvest index by 17%, and water-use efficiency by 7%. Seed size was not affected by either fertilizer or watering treatments. More than 65% of the total root dry matter was distributed in the upper 20 cm of the profile in all watering treatments. However, the two drought stress treatments, especially the mild drought stress, had a greater proportion of root dry matter located in the deeper soil layers. The overall coefficient of variation for seed yield was low at 5.3%, suggesting good repeatability of the treatments. Drought stress imposed in this culture system affected yield components similarly to what is observed in the field, with pod number being the component most strongly affected. This system should be useful for identifying variation among soybean lines for a wide variety of traits related to drought tolerance.

Below-ground competition favors character convergence but not character displacement in root traits

Character displacement can play a major role in species ecology and evolution; however, research testing whether character displacement can influence the evolution of root traits in plant systems remains scarce in the literature. Here we investigated the potential that character displacement may influence the evolution of root traits using two closely related morning glory species, Ipomoea purpurea and Ipomoea hederacea. We performed a field experiment where we grew the common morning glory, I. purpurea, in the presence and absence of competition from I. hederacea and examined the potential that the process of character displacement could influence the evolution of root traits. We found maternal line variation in root phenotypes and evidence that below-ground competition acts as an agent of selection on these traits. Our test of character displacement, however, showed evidence of character convergence on our measure of root architecture rather than displacement. These results suggest that plants may be constrained by their local environments to express a phenotype that enhances fitness. Therefore, the conditions of the competitive environment experienced by a plant may influence the potential for character convergence or displacement to influence the evolution of root traits.

Semiautomated 3D Root Segmentation and Evaluation Based on X-Ray CT Imagery

BACKGROUND

Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots.

OBJECTIVE

For semiautomated and robust root system segmentation from CTX data, we propose the RootForce approach, which is an extension of Frangi's "multi-scale vesselness" method and integrates a 3D local variance. It allows a precise delineation of roots with diameters down to several μm in pots with varying diameters. Additionally, RootForce is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels.

RESULTS

Using CTX volume data of full-grown bean plants as well as time-resolved (3D + time) growth studies of cassava plants, RootForce produces similar (and much faster) results compared to manual segmentation of the regarded root architectures. Furthermore, RootForce enables the user to obtain traits not possible to be calculated before, such as total root volume (V root), total root length (L root), root volume over depth, root growth angles (θ min, θ mean, and θ max), root surrounding soil density D soil, or form fraction F. Discussion. The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.

Root phenotyping: important and minimum information required for root modeling in crop plants

As plants cannot relocate, they require effective root systems for water and nutrient uptake. Root development plasticity enables plants to adapt to different environmental conditions. Research on improvements in crop root systems is limited in comparison with that in shoots as the former are difficult to image. Breeding more effective root systems is proposed as the "second green revolution". There are several recent publications on root system architecture (RSA), but the methods used to analyze the RSA have not been standardized. Here, we introduce traditional and current root-imaging methods and discuss root structure phenotyping. Some important root structures have not been standardized as roots are easily affected by rhizosphere conditions and exhibit greater plasticity than shoots; moreover, root morphology significantly varies even in the same genotype. For these reasons, it is difficult to define the ideal root systems for breeding. In this review, we introduce several types of software to analyze roots and identify important root parameters by modeling to simplify the root system characterization. These parameters can be extracted from photographs captured in the field. This modeling approach is applicable to various legacy root data stored in old or unpublished formats. Standardization of RSA data could help estimate root ideotypes.

Root phenotyping: important and minimum information required for root modeling in crop plants

As plants cannot relocate, they require effective root systems for water and nutrient uptake. Root development plasticity enables plants to adapt to different environmental conditions. Research on improvements in crop root systems is limited in comparison with that in shoots as the former are difficult to image. Breeding more effective root systems is proposed as the "second green revolution". There are several recent publications on root system architecture (RSA), but the methods used to analyze the RSA have not been standardized. Here, we introduce traditional and current root-imaging methods and discuss root structure phenotyping. Some important root structures have not been standardized as roots are easily affected by rhizosphere conditions and exhibit greater plasticity than shoots; moreover, root morphology significantly varies even in the same genotype. For these reasons, it is difficult to define the ideal root systems for breeding. In this review, we introduce several types of software to analyze roots and identify important root parameters by modeling to simplify the root system characterization. These parameters can be extracted from photographs captured in the field. This modeling approach is applicable to various legacy root data stored in old or unpublished formats. Standardization of RSA data could help estimate root ideotypes.

Method for Characterization of Root Traits in Chickpea Germplasm for Legume Genomics and Breeding

A semi-hydroponic phenotyping platform was constructed using inexpensive and easily obtained materials for characterizing root trait variability in a large set of chickpea (Cicer arietinum) germplasm. The system was designed to accommodate a large number of plants in a small area allowing relatively deeper root development, and thus serves as a high-throughput phenotyping tool for studying root dynamic growth. The root trait quantitative platform could provide accurate phenotyping data for parameterizing root models and for genome-wide association analyses or mapping studies of quantitative trait loci.

A Comparative Analysis of Quantitative Metrics of Root Architecture

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convex hull volume, and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N₂) is fixed into bioavailable ammonia (NH₃) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit (R ² = 0.99). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

Transcriptome profiling of genes involved in nutrient uptake regulated by phosphate-solubilizing bacteria in pepper (Capsicum annuum L.)

Improving nutrient absorption in pepper has become a vital prerequisite for growth to produce a sustainable yield. In this study, transcriptome gene expression in pepper inoculated with two types of phosphate-solubilizing bacteria (PSB) and grown under low and high nutrient levels (LN and HN) was analyzed. Results showed that the root length increased when pepper was grown under LN; however, the root structure was intensively tight under HN. Our data revealed that the roots preferred horizontal growth than longitudinal growth under HN. PSB strains 'M01' and 'N3' significantly (P < 0.01) increased the P uptake by 70.44% and 98.20%, respectively, but decreased the Ca²⁺ content by 8.96% and 9.13%, respectively, compared with the control (L1). Although no remarkable difference was detected in the chlorophyll content, inoculation with the two PSB strains decreased the Fe³⁺ content in pepper under HN. The total clean sequenced data from samples ranged between 5,923,659,118 and 9,955,045,953 bp. Transcriptome profiling revealed 320 upregulated and 449 downregulated genes in L3 versus L1 and 468 upregulated and 532 downregulated genes in L4 versus L1. Gene ontology analysis revealed that the biological processes, including response to stress and secondary metabolic process, were involved. Several pathways were subordinate to glycosphingolipid biosynthesis and linoleic acid and nitrogen metabolisms. Analysis of the eukaryotic orthologous group function revealed that most differential genes were attributed to RNA processing and modification, transcription, and signal transduction. Our results provided new insights into the molecular mechanism related to nutrient uptake in pepper inoculated with PSBs.

Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package "implant" in R for both robust feature extraction and functional data analysis. For image processing, the "implant" package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.

A Physio-Morphological Trait-Based Approach for Breeding Drought Tolerant Wheat

In the past, there have been drought events in different parts of the world, which have negatively influenced the productivity and production of various crops including wheat (Triticum aestivum L.), one of the world's three important cereal crops. Breeding new high yielding drought-tolerant wheat varieties is a research priority specifically in regions where climate change is predicted to result in more drought conditions. Commonly in breeding for drought tolerance, grain yield is the basis for selection, but it is a complex, late-stage trait, affected by many factors aside from drought. A strategy that evaluates genotypes for physiological responses to drought at earlier growth stages may be more targeted to drought and time efficient. Such an approach may be enabled by recent advances in high-throughput phenotyping platforms (HTPPs). In addition, the success of new genomic and molecular approaches rely on the quality of phenotypic data which is utilized to dissect the genetics of complex traits such as drought tolerance. Therefore, the first objective of this review is to describe the growth-stage based physio-morphological traits that could be targeted by breeders to develop drought-tolerant wheat genotypes. The second objective is to describe recent advances in high throughput phenotyping of drought tolerance related physio-morphological traits primarily under field conditions. We discuss how these strategies can be integrated into a comprehensive breeding program to mitigate the impacts of climate change. The review concludes that there is a need for comprehensive high throughput phenotyping of physio-morphological traits that is growth stage-based to improve the efficiency of breeding drought-tolerant wheat.