RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Emissions of volatile organic compounds from aboveground and belowground parts of rapeseed (Brassica napus L.) and tomato (Solanum lycopersicum L.)

Root systems represent a source of Volatile Organic Compounds (VOCs) that may significantly contribute to the atmospheric VOC emissions from agroecosystems and shape soil microbial activity. To gain deeper insights into the role of roots in the VOC emissions from crops, we developed a dynamic chamber with isolated aboveground and belowground compartments, allowing for simultaneous measurements of VOC fluxes from both compartments in controlled conditions. We continuously monitored VOC emissions from intact plants of rapeseed (Brassica napus L.) and tomato (Solanum lycopersicum L.) i) over 24 h when plants were rooted in soil, and ii) over 6 h following soil removal. The measurements were performed using a highly sensitive Proton Transfer Reaction - Time of Flight - Mass Spectrometer and a Thermic Desorption- Gas Chromatography - Mass Spectrometer. Net VOC emissions measured at the soil surface represented <5 % of the aboveground emissions and were higher during the day than at night. However, when soil was removed, belowground VOC emissions became up to two times higher than aboveground emissions. This large increase in VOC emissions from roots observed after soil removal was almost exclusively due to methanol emissions. Differences in VOC composition between plant species were also detected with and without soil: rapeseed emitted more sulphurous and nitrogenous compounds and tomato more mono- and poly-unsaturated hydrocarbons. Our results suggest that roots may be a largely underestimated VOC source and that the soil is a strong sink for root-borne methanol. Root VOC emissions should be considered when agricultural practices involve roots excavation.

Root traits of soybeans exposed to polyethylene films, polypropylene fragments, and biosolids

Biosolid use imports microplastics into the rhizosphere where they may interfere with root-soil-microbial interactions and cause morphological adaptations in crop root systems. Few studies have examined the response of crop roots to microplastics at documented soil concentrations, and many studies collect root traits using destructive techniques. Hence, there is little information on when and how microplastics effect the physical structure of root systems. Using the rhizobox method, soybeans (Glycine max) were grown in soil amended with biosolid microplastic mimics (polyethylene film or polypropylene fragments at 2,000 and 15,000 particles/kg dry soil) or biosolids and imaged weekly until maturity (11 weeks) using a custom scanner system. Plant biomass increased in the polyethylene treatments and decreased in the high concentration polypropylene treatment. Relative to the Control, polyethylene treatments had larger root length, reduced root diameters, reached maturity faster, had deeper root systems, and had a greater number of lateral roots. In contrast, polypropylene treatments had a mixed response, with high concentrations eliciting a lower root length, fewer laterals, and a more vertical root orientation. Segmented linear regression revealed that root growth in the Control and Biosolid treatments continued through the course of the experiment, while the microplastic treatments reached maturity up to two weeks earlier. Imagery revealed that microplastics elicited deeper rooting depth within the first week and differences in all root traits were evident by the development of the first trifoliate leaflets. Microplastic effects on root traits at early life stages suggest soil physiological drivers, while increased branching frequency and lower lateral elongation are suggestive of changes in soil nitrogen availability. The minimal difference in root traits in the biosolid treatment may be attributable to differences in microplastic properties or counteractive effects by other biosolid constituents.

Adaptive mechanisms of wheat cultivars to lead toxicity through enhanced oxidative defense, ionomic redistribution, and anatomical modifications

Lead (Pb) contamination is a critical environmental issue that poses a substantial threat to agricultural sustainability and crop productivity, particularly for staple crops like wheat (Triticum aestivum L.). This study investigates the differential physiological, biochemical, and anatomical responses of two wheat cultivars, SKD-1 and Borlaug-16, under Pb stress (100 mg/kg Pb for 21 days). Borlaug-16 displayed a notable tolerance to Pb toxicity, evidenced by a significant increase in total biomass, including a 41.22% rise in shoot turgid weight and a 23.37% increase in root turgid weight, alongside a 57.72% enhancement in root cortex thickness. This cultivar also showed increased antioxidant enzyme activities, such as catalase and peroxidase, and a better ionomic balance, maintaining higher levels of essential minerals like Ca in leaf tissues while effectively accumulating Pb and other trace elements in roots. In contrast, SKD-1 suffered from a more substantial reduction in essential minerals and weaker anatomical and biochemical defenses. The study's novelty lies in providing an integrated approach to understanding wheat cultivar-specific adaptations to Pb stress, suggesting Borlaug-16 as a promising candidate for cultivation in Pb-contaminated soils. These findings underscore the importance of developing Pb-tolerant cultivars to ensure sustainable wheat production in polluted environments.

Contribution of mycorrhizal symbiosis and root strategy to red clover aboveground biomass under nitrogen addition and phosphorus distribution

Soil nutrients exhibit heterogeneity in their spatial distribution, presenting challenges to plant acquisition. Notably, phosphorus (P) heterogeneity is a characteristic feature of soil, necessitating the development of adaptive strategies by plants to cope with this phenomenon. To address this, fully crossed three-factor experiments were conducted using red clover within rhizoboxes. Positions of P in three conditions, included P even distribution (even P), P close distribution (close P), and P far distribution (far P). Concurrently, N addition was two amounts(0 and 20 mg kg- 1), both with and without AMF inoculation. The findings indicated a decrease in aboveground biomass attributable to uneven P distribution, whereas N and AMF demonstrated the potential to affect aboveground biomass. In a structural equation model, AMF primarily increased aboveground biomass by enhancing nodule number and specific leaf area (SLA). In contrast, N addition improved aboveground biomass through increased nodule number or direct effects. Subsequently, a random forest model indicated that under the far P treatment, fine root length emerged as the primary factor affecting aboveground biomass, followed by thickest root length. Conversely, in the even P treatment, the thickest root length was of paramount importance. In summary, when confronted with uneven P distribution, clover plants adopted various root foraging strategies. AMF played a pivotal role in elevating nodule number, and SLA.

Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

Automated seminal root angle measurement with corrective annotation

Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify single nucleotide polymorphisms (SNPs) significantly associated with angle and length, shedding light on the genetic basis of root architecture.

Evaluating waterlogging stress response and recovery in barley (Hordeum vulgare L.): an image-based phenotyping approach

Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.

Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18

Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa (Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model's overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).

Early exposure to phosphorus starvation induces genetically determined responses in Sorghum bicolor roots

KEY MESSAGE

We identified novel physiological and genetic responses to phosphorus starvation in sorghum diversity lines that augment current knowledge of breeding for climate-smart crops in Europe. Phosphorus (P) deficiency and finite P reserves for fertilizer production pose a threat to future global crop production. Understanding root system architecture (RSA) plasticity is central to breeding for P-efficient crops. Sorghum is regarded as a P-efficient and climate-smart crop with strong adaptability to different climatic regions of the world. Here we investigated early genetic responses of sorghum RSA to P deficiency in order to identified genotypes with interesting root phenotypes and responses under low P. A diverse set of sorghum lines (n = 285) was genotyped using DarTSeq generating 12,472 quality genome wide single-nucleotide polymorphisms. Root phenotyping was conducted in a paper-based hydroponic rhizotron system under controlled greenhouse conditions with low and optimal P nutrition, using 16 RSA traits to describe genetic and phenotypic variability at two time points. Genotypic and phenotypic P-response variations were observed for multiple root traits at 21 and 42 days after germination with high broad sense heritability (0.38-0.76). The classification of traits revealed four distinct sorghum RSA types, with genotypes clustering separately under both low and optimal P conditions, suggesting genetic control of root responses to P availability. Association studies identified quantitative trait loci in chromosomes Sb02, Sb03, Sb04, Sb06 and Sb09 linked with genes potentially involved in P transport and stress responses. The genetic dissection of key factors underlying RSA responses to P deficiency could enable early identification of P-efficient sorghum genotypes. Genotypes with interesting RSA traits for low P environments will be incorporated into current sorghum breeding programs for later growth stages and field-based evaluations.

Evolutionary history and root trait coordination predict nutrient strategy in tropical legume trees

Plants express diverse nutrient use and acquisition traits, but it is unclear how trait combinations at the species level are constrained by phylogeny, trait coordination, or trade-offs in resource investment. One trait - nitrogen (N) fixation - is assumed to correlate with other traits and used to define plant functional groups, despite potential confounding effects of phylogeny. We quantified growth, carbon metabolism, fixation rate, root phosphatase activity (RPA), mycorrhizal colonization, and leaf and root morphology/chemistry across 22 species of fixing and nonfixing tropical Fabaceae trees under common conditions. Belowground trait variation was high even among closely related species, and most traits displayed a phylogenetic signal, including N-fixation rate and nodule biomass. Across species, we observed strong positive correlations between physiological traits such as RPA and root respiration. RPA increased ~ fourfold per unit increase in fixation, supporting the debated hypothesis that N-fixers 'trade' N for phosphatases to enhance phosphorus acquisition. Specific root length and root N differed between functional groups, though for other traits, apparent differences became nonsignificant after accounting for phylogenetic nonindependence. We conclude that evolutionary history, trait coordination, and fixation ability contribute to nutrient trait expression at the species level, and recommend explicitly considering phylogeny in analyses of functional groupings.

Desert plants, arbuscular mycorrhizal fungi and associated bacteria: Exploring the diversity and role of symbiosis under drought

Desert plants, such as Agave tequilana, A. salmiana and Myrtillocactus geometrizans, can survive harsh environmental conditions partly due to their symbiotic relationships with microorganisms, including arbuscular mycorrhizal fungi (AMF). Interestingly, some of these fungi also harbour endosymbiotic bacteria. Our research focused on investigating the diversity of these AMFs and their associated bacteria in these plants growing in arid soil. We found that agaves have a threefold higher AMF colonization than M. geometrizans. Metabarcoding techniques revealed that the composition of AMF communities was primarily influenced by the plant host, while the bacterial communities were more affected by the specific plant compartment or niche they inhabited. We identified both known and novel endofungal bacterial taxa, including Burkholderiales, and confirmed their presence within AMF spores using multiphoton microscopy. Our study also explored the effects of drought on the symbiosis between A. tequilana and AMF. We discovered that the severity of drought conditions could modulate the strength of this symbiosis and its outcomes for the plant holobiont. Severe drought conditions prevented the formation of this symbiosis, while moderate drought conditions promoted it, thereby conferring drought tolerance in A. tequilana. This research sheds light on the diversity of AMF and associated bacteria in Crassulacean Acid Metabolism (CAM) plants and underscores the crucial role of drought as a factor modulating the symbiosis between A. tequilana and AMF. Further research is needed to understand the role of endofungal bacteria in this response.

An advanced three-dimensional phenotypic measurement approach for extracting Ginkgo root structural parameters based on terrestrial laser scanning

The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R 2) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: R 2 = 0.71, RMSE = 2.20 cm; TIN-based: R 2 = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.

Do Lignin Nanoparticles Pave the Way for a Sustainable Nanocircular Economy? Biostimulant Effect of Nanoscaled Lignin in Tomato Plants

Agriculture has a significant environmental impact and is simultaneously called to major challenges, such as responding to the need to develop more sustainable cropping systems with higher productivity. In this context, the present study aimed to obtain lignin nanoparticles (LNs) from pomace, a waste product of the olive oil chain, to be used as a nanobiostimulant in tomato plants. The biostimulant effect of this biopolymer is known, but its reduction to nanometer size can emphasize this property. Tomato plants were subjected to different LN dosages (25, 50, and 100 mg L-1) by foliar application, and inductive effects on photosynthetic machinery, aerial and root biomass production, and root morphology were observed. The treated plants showed increased efficiency in catching and using light, while they reduced the fraction dissipated as heat or potentially toxic to cells for the possibility of creating reactive oxygen species (ROS). Finally, this benefit was matched by increased pigment content and a stimulatory action on the content of nitrogen (NBI) and antioxidant substances such as flavonoids. In conclusion, the present study broadens the horizon of substances with biostimulant action by demonstrating the validity and efficacy of nanobiostimulants obtained from biological residues from the olive oil production chain.

Unraveling root and rhizosphere traits in temperate maize landraces and modern cultivars: Implications for soil resource acquisition and drought adaptation

A holistic understanding of plant strategies to acquire soil resources is pivotal in achieving sustainable food security. However, we lack knowledge about variety-specific root and rhizosphere traits for resource acquisition, their plasticity and adaptation to drought. We conducted a greenhouse experiment to phenotype root and rhizosphere traits (mean root diameter [Root D], specific root length [SRL], root tissue density, root nitrogen content, specific rhizosheath mass [SRM], arbuscular mycorrhizal fungi [AMF] colonization) of 16 landraces and 22 modern cultivars of temperate maize (Zea mays L.). Our results demonstrate that landraces and modern cultivars diverge in their root and rhizosphere traits. Although landraces follow a 'do-it-yourself' strategy with high SRLs, modern cultivars exhibit an 'outsourcing' strategy with increased mean Root Ds and a tendency towards increased root colonization by AMF. We further identified that SRM indicates an 'outsourcing' strategy. Additionally, landraces were more drought-responsive compared to modern cultivars based on multitrait response indices. We suggest that breeding leads to distinct resource acquisition strategies between temperate maize varieties. Future breeding efforts should increasingly target root and rhizosphere economics, with SRM serving as a valuable proxy for identifying varieties employing an outsourcing resource acquisition strategy.

A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species-crimson clover (Trifolium incarnatum) and rye (Secale cereale)-and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments.

Belowground plant allocation regulates rice methane emissions from degraded peat soils

Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO2 and N2O emissions from peat. However, wet rice cultivation can release considerable methane (CH4). Water table and soil management strategies may enhance rice yield and minimize CH4 emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH4-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH4. Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH4 and BGB were positively related, with BGB explaining 60% of the variation in CH4 but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH4 likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH4 emissions from wet rice cultivation in degraded peatlands.

Growth and antioxidant responses to water stress in eggplant MAGIC population parents, F1 hybrids and a subset of recombinant inbred lines

BACKGROUND

The generation of new eggplant (Solanum melongena L.) cultivars with drought tolerance is a main challenge in the current context of climate change. In this study, the eight parents (seven of S. melongena and one of the wild relative S. incanum L.) of the first eggplant MAGIC (Multiparent Advanced Generation Intercrossing) population, together with four F1 hybrids amongst them, five S5 MAGIC recombinant inbred lines selected for their genetic diversity, and one commercial hybrid were evaluated in young plant stage under water stress conditions (30% field capacity; FC) and control conditions (100% FC). After a 21-day treatment period, growth and biomass traits, photosynthetic pigments, oxidative stress markers, antioxidant compounds, and proline content were evaluated.

RESULTS

Significant effects (p < 0.05) were observed for genotype, water treatments and their interaction in most of the traits analyzed. The eight MAGIC population parental genotypes displayed a wide variation in their responses to water stress, with some of them exhibiting enhanced root development and reduced foliar biomass. The commercial hybrid had greater aerial growth compared to root growth. The four F1 hybrids among MAGIC parents differed in their performance, with some having significant positive or negative heterosis in several traits. The subset of five MAGIC lines displayed a wide diversity in their response to water stress.

CONCLUSION

The results show that a large diversity for tolerance to drought is available among the eggplant MAGIC materials, which can contribute to developing drought-tolerant eggplant cultivars.

RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs

Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.

ZmPILS6 is an auxin efflux carrier required for maize root morphogenesis

Plant root systems play a pivotal role in plant physiology and exhibit diverse phenotypic traits. Understanding the genetic mechanisms governing root growth and development in model plants like maize is crucial for enhancing crop resilience to drought and nutrient limitations. This study focused on identifying and characterizing ZmPILS6, an annotated auxin efflux carrier, as a key regulator of various crown root traits in maize. ZmPILS6-modified roots displayed reduced network area and suppressed lateral root formation, which are desirable traits for the "steep, cheap, and deep" ideotype. The research revealed that ZmPILS6 localizes to the endoplasmic reticulum and plays a vital role in controlling the spatial distribution of indole-3-acetic acid (IAA or "auxin") in primary roots. The study also demonstrated that ZmPILS6 can actively efflux IAA when expressed in yeast. Furthermore, the loss of ZmPILS6 resulted in significant proteome remodeling in maize roots, particularly affecting hormone signaling pathways. To identify potential interacting partners of ZmPILS6, a weighted gene coexpression analysis was performed. Altogether, this research contributes to the growing knowledge of essential genetic determinants governing maize root morphogenesis, which is crucial for guiding agricultural improvement strategies.

Deep learning-based association analysis of root image data and cucumber yield

The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.

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.

Distinct growth patterns in seedling and tillering wheat plants suggests a developmentally restricted role of HYD2 in salt-stress response

KEY MESSAGE

Mutants lacking functional HYD2 homoeologs showed improved seedling growth, but comparable or increased susceptibility to salt stress in tillering plants, suggesting a developmentally restricted role of HYD2 in salt response. Salinity stress threatens global food security by reducing the yield of staple crops such as wheat (Triticum ssp.). Understanding how wheat responds to salinity stress is crucial for developing climate resilient varieties. In this study, we examined the interplay between carotenoid metabolism and the response to salt (NaCl) stress, a specific form of salinity stress, in tetraploid wheat plants with mutations in carotenoid β-hydroxylase 1 (HYD1) and HYD2. Our investigation encompassed both the vulnerable seedling stage and the more developed tillering stage of wheat plant growth. Mutant combinations lacking functional HYD2 homoeologs, including hyd-A2 hyd-B2, hyd-A1 hyd-A2 hyd-B2, hyd-B1 hyd-A2 hyd-B2, and hyd-A1 hyd-B1 hyd-A2 hyd-B2, had longer first true leaves and slightly enhanced root growth during germination under salt stress compared to the segregate wild-type (control) plants. Interestingly, these mutant seedlings also showed decreased levels of neoxanthin and violaxanthin (xanthophylls derived from β-carotene) and an increase in β-carotene in roots. However, tillering hyd mutant and segregate wild-type plants generally did not differ in their height, tiller count, and biomass production under acute or prolonged salt stress, except for decreases in these parameters observed in the hyd-A1 hyd-B1 hyd-A2 hyd-B2 mutant that indicate its heightened susceptibility to salt stress. Taken together, these findings suggest a significant, yet developmentally restricted role of HYD2 homoeologs in salt-stress response in tetraploid wheat. They also show that hyd-A2 hyd-B2 mutant plants, previously demonstrated for possessing enriched nutritional (β-carotene) content, maintain an unimpaired ability to withstand salt stress.

The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower

Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance.

Fast and Efficient Root Phenotyping via Pose Estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Alfalfa Responses to Intensive Soil Compaction: Effects on Plant and Root Growth, Phytohormones and Internal Gene Expression

The perennial legume alfalfa (Medicago sativa L.) is of high value in providing cheap and high-nutritive forages. Due to a lack of tillage during the production period, the soil in which alfalfa grows prunes to become compacted through highly mechanized agriculture. Compaction deteriorates the soil's structure and fertility, leading to compromised alfalfa development and productivity. However, the way alfalfa responses to different levels of soil compaction and the underlying molecular mechanism are still unclear. In this study, we systematically evaluated the effects of gradient compacted soil on the growth of different cultivars of alfalfa, especially the root system architecture, phytohormones and internal gene expression profile alterations. The results showed that alfalfa growth was facilitated by moderate soil compaction, but drastically inhibited when compaction was intensified. The inhibition effect was universal across different cultivars, but with different severity. Transcriptomic and physiological studies revealed that the expression of a set of genes regulating the biosynthesis of lignin and flavonoids was significantly repressed in compaction treated alfalfa roots, and this might have resulted in a modified secondary cell wall and xylem vessel formation. Phytohormones, like ABA, are supposed to play pivotal roles in the regulation of the overall responses. These findings provide directions for the improvement of field soil management in alfalfa production and the molecular breeding of alfalfa germplasm with better soil compaction resilience.

Genome-wide association study of drought tolerance in wheat (Triticum aestivum L.) identifies SNP markers and candidate genes

Drought stress poses a severe threat to global wheat production, necessitating an in-depth exploration of the genetic basis for drought tolerance associated traits. This study employed a 90 K SNP array to conduct a genome-wide association analysis, unravelling genetic determinants of key traits related to drought tolerance in wheat, namely plant height, root length, and root and shoot dry weight. Using the mixed linear model (MLM) method on 125 wheat accessions subjected to both well-watered and drought stress treatments, we identified 53 SNPs significantly associated with stress susceptibility (SSI) and tolerance indices (STI) for the targeted traits. Notably, chromosomes 2A and 3B stood out with ten and nine associated markers, respectively. Across 17 chromosomes, 44 unique candidate genes were pinpointed, predominantly located on the distal ends of 1A, 1B, 1D, 2A, 3A, 3B, 4A, 6A, 6B, 7A, 7B, and 7D chromosomes. These genes, implicated in diverse functions related to plant growth, development, and stress responses, offer a rich resource for future investigation. A clustering pattern emerged, notably with seven genes associated with SSI for plant height and four genes linked to both STI of plant height and shoot dry weight, converging on specific regions of chromosome arms of 2AS and 3BL. Additionally, shared genes encoding polygalacturonase, auxilin-related protein 1, peptide deformylase, and receptor-like kinase underscored the interconnectedness between plant height and shoot dry weight. In conclusion, our findings provide insights into the molecular mechanisms governing wheat drought tolerance, identifying promising genomic loci for further exploration and crop improvement strategies.

Cyber-agricultural systems for crop breeding and sustainable production

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.

Paenibacillus lentimorbus alleviates nutrient deficiency-induced stress in Zea mays by modulating root system architecture, auxin signaling, and metabolic pathways

KEY MESSAGE

Paenibacillus lentimorbus reprograms auxin signaling and metabolic pathways for modulating root system architecture to mitigate nutrient deficiency in maize crops. The arable land across the world is having deficiency and disproportionate nutrients, limiting crop productivity. In this study, the potential of plant growth-promoting rhizobacteria (PGPR) viz., Pseudomonas putida, Paenibacillus lentimorbus, and their consortium was explored for growth promotion in maize (Zea mays) under nutrient-deficient conditions. PGPR inoculation improved the overall health of plants under nutrient-deficient conditions. The PGPR inoculation significantly improved the root system architecture and also induced changes in root cortical aerenchyma. Based on plant growth and physiological parameters inoculation with P. lentimorbus performed better as compared to P. putida, consortium, and uninoculated control. Furthermore, expression of auxin signaling (rum1, rul1, lrp1, rtcs, rtcl) and root hair development (rth)-related genes modulated the root development process to improve nutrient acquisition and tolerance to nutrient-deficient conditions in P. lentimorbus inoculated maize plants. Further, GC-MS analysis indicated the involvement of metabolites including carbohydrates and organic acids due to the interaction between maize roots and P. lentimorbus under nutrient-deficient conditions. These findings affirm that P. lentimorbus enhance overall plant growth by modulating the root system of maize to provide better tolerance to nutrient-deficient condition.

Whole-genome sequencing of Fusarium spp. causing sugarcane root rot on both chewing cane and sugar-making cane

Previously we isolated three Fusarium strains (a F. sacchari strain namely GXUF-1, and another two F. commune strains namely GXUF-2 and GXUF-3), and we verified that GXUF-3 was able to cause sugarcane root rot to the chewing cane cultivar Badila. Considering that Fusarium spp. are a group of widely distributed fungal pathogens, we tested whether these three Fusarium isolates were able to cause root rot to Badila as well as sugar-making cane cultivar (Guitang42), using a suitable inoculation method established based on infection assays using Badila. We found that the three Fusarium strains were able to cause root rot symptoms to both Badila and Guitang42, to different extents. To better investigate the potential pathogenicity mechanisms, we performed Illumina high-throughput sequencing and analyzed the whole genomic sequence data of these three Fusarium strains. The results reveal that the assembly sizes of the three Fusarium strains were in a range of 44.7-48.2 Mb, with G + C contents of 48.0-48.5%, and 14,154-15,175 coding genes. The coding genes were annotated by multiple public databases, and potential pathogenic genes were predicted using proprietary databases (such as PHI, DFVF, CAZy, etc.). Furthermore, based on evolutionary analysis of the coding sequence, we found that contraction and expansion of gene families occurred in the three Fusarium strains. Overall, our results suggest a potential risk that the root rot disease may occur to the sugar-making canes although it was initially spotted from fruit cane, and provide clues to understand the pathogenic mechanisms of Fusarium spp. causing sugarcane root rot.

Plant-soil feedback and plant invasion: effect of soil conditioning on native and invasive Prosopis species using the plant functional trait approach

Introduction

Invasive species have been identified as a major threat to native biodiversity and ecosystem functioning worldwide due to their superiority in spread and growth. Such superiority is explained by the invasional meltdown phenomena, which suggests that invasive species facilitate the establishment of more invasive species rather than native species by modifying the plant-soil feedback (PSF).

Methods

We conducted a two-phase plant-soil feedback experiment using the native Prosopis cineraria and the invasive Prosopis juliflora in Oman. Firstly, we conditioned the soil by planting seedlings of native species, invasive species, native and invasive species "mixed", and unconditioned soil served as a control. Secondly, we tested the feedback of these four conditioned soil on the two species separately by measuring the productivity (total biomass) and the performance in the form of plant functional traits (plant height, specific leaf area (SLA), leaf nitrogen content (Nmass), leaf carbon content (Cmass) and specific root length (SRL) of native and invasive species as well as the nutrient availability in soil (soil organic carbon (SOC) and soil total nitrogen (STN)).

Results and discussion

We found that the native species produced more biomass, best performance, and higher SOC and STN when grown in soil conditioned by native species, additionally, it gave lower biomass, reduced performance, and lower SOC and STN when grown in the soil conditioned by invasive and mixed species. These results suggest negative PSF for native species and positive PSF for invasive species in the soil conditioned by invasive species, which can be considered as red flag concerning the restoration of P. cineraria as an important native species in Oman, as such positive PSF of the invasive species P. juliflora will inhibit the regeneration of P. cineraria.

Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

Comprehensive analysis of OsJAZ gene family deciphers rhizobacteria-mediated nutrient stress modulation in rice

The JASMONATE-ZIM DOMAIN (JAZ) repressors are crucial proteins in jasmonic acid signaling pathway that are critical for plant growth. Therefore, the present study aimed to identify and characterize OsJAZs in the rice genome, revealing their structural attributes, regulatory elements, miRNA interactions, and subcellular localization. 23 JAZ transcripts across the 6 chromosomes of rice genome were identified having conserved domains and different physiochemical characteristics. Phylogenetically classified into five clades, they showed highest syntenic relationship with P. virgatum. The non-synonymous/synonymous values ranged from 0.44 to 1.21 suggesting purifying/stabilizing selection in OsJAZs. The study examined the 1.5 kb promoter region for cis-regulatory elements, and also identified 92 miRNAs targets. Furthermore, homology modeling provided insights into the 3D-structures of JAZ proteins while in-silico gene expression analysis revealed their functional diversity in various tissues and developmental stages. Additionally, qRT-PCR analysis highlighted their involvement in stress adaptation to sub-optimum nutrient conditions induced by plant-beneficial rhizobacteria Bacillus amyloliquefaciens (SN13) in two rice varieties. Distinct OsJAZ expression patterns in the two varieties correlated with altered root architecture, xylem structure, and lignification. These findings affirmed that specific up-or down-regulation of OsJAZs might play critical role in SN13 induced changes in the two varieties that enabled them to survive under stress.

Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

Root phenotypes for improved nitrogen capture

Background

Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer.

Scope

Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes.

Conclusions

Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.

Multi-year belowground data of minirhizotron facilities in Selhausen

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods.

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S. miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S. miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K-means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S. miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation (R2 = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices.

Nitrogen Use Efficiency in Oil Palm Seedlings: Unraveling the Untapped Potential of Elevated External Ammonium Supply

Ammonium (NH4+) is an essential nitrogen source for plants, but excessive exposure can trigger stress responses that vary among and within different plant species. This study investigated the phenotypic variations in response to ammonium nutrition in five oil palm genotypes seedlings. Nitrate nutrition was used as a reference for a non-stressful condition, and three different nitrogen concentrations (5, 10, and 15 mM) were examined. Control groups without external nitrogen application were included for each genotype. Several parameters were analyzed, including plant growth, root length, gas exchange, fluorescence, chlorophyll, reducing sugars, amino acids, proteins, and nitrogen uptake. The results revealed a significant genotype effect, particularly between the interspecific OxG hybrid and the Elaeis guinensis genotypes. Ammonium nutrition increased shoot growth in all genotypes compared to nitrate nutrition. Additionally, there was a trend towards increased primary root length, amino acids, proteins, and nitrogen uptake under ammonium supply. These findings are promising, particularly considering the recommendation to use ammonium with inhibitors for environmental sustainability.

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

The Comparative Root System Architecture of Declining and Non-Declining Trees in Two Apple Orchards in New York

Rapid apple decline is a phenomenon characterized by a weakening of young apple trees in high density orchards, often followed by their quick collapse. The nature of this phenomenon remains unclear. In this work, we investigated the root system architecture (RSA) of declining and non-declining apple trees in two orchards in New York State. High-density orchard A consisted of 4-year-old 'Honeycrisp' on 'Malling 9 Nic29', and conventional orchard B consisted of 8-year-old 'Fuji' on 'Budagovsky 9'. In both orchards, a negative correlation (-0.4--0.6) was observed between RSA traits and decline symptoms, suggesting that declining trees have weaker root systems. Scion trunk diameter at the graft union, total root length, and the length of fine and coarse roots were significantly (p < 0.05) reduced in declining trees in both orchards. Additionally, internal trunk necrosis at, above, and below the graft union was observed in declining trees in orchard A but not in orchard B. Finally, latent viruses were not associated with decline, as their occurrence was documented in declining and non-declining trees in orchard A, but not in orchard B. Together, these results showed weakened root systems of declining trees, suggesting that these trees may experience deficiencies in water and nutrient uptake, although distinct RSA and trunk health traits between the two orchards were noticeable.

Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots

The root is an important organ for crops to absorb water and nutrients. Complete and accurate acquisition of root phenotype information is important in root phenomics research. The in situ root research method can obtain root images without destroying the roots. In the image, some of the roots are vulnerable to soil shading, which severely fractures the root system and diminishes its structural integrity. The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored. Therefore, based on the in situ root image of cotton, this study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation. It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two. The research results show that the improved UNet model has an accuracy of 99.2%, mIOU of 87.03%, and F1 of 92.63%. The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%. This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network. It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems, also realizes the restoration of the integrity of the in situ root image, and provides a new method for in situ root phenotype study.

High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements

A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. We also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.

Stress-induced F-Box protein-coding gene OsFBX257 modulates drought stress adaptations and ABA responses in rice

F-box (FB) proteins that form part of SKP1-CUL1-F-box (SCF) type of E3 ubiquitin ligases are important components of plant growth and development. Here we characterized OsFBX257, a rice FB protein-coding gene that is differentially expressed under drought conditions and other abiotic stresses. Population genomics analysis suggest that OsFBX257 shows high allelic diversity in aus accessions and has been under positive selection in some japonica, aromatic and indica cultivars. Interestingly, allelic variation at OsFBX257 in aus cultivar Nagina22 is associated with an alternatively spliced transcript. Conserved among land plants, OsFBX257 is a component of the SCF complex, can form homomers and interact molecularly with the 14-3-3 rice proteins GF14b and GF14c. OsFBX257 is co-expressed in a network involving protein kinases and phosphatases. We show that OsFBX257 can bind the kinases OsCDPK1 and OsSAPK2, and that its phosphorylation can be reversed by phosphatase OsPP2C08. OsFBX257 expression level modulates root architecture and drought stress tolerance in rice. OsFBX257 knockdown (OsFBX257^KD ) lines show reduced total root length and depth, crown root number, panicle size and survival under stress. In contrast, its overexpression (OsFBX257^OE ) increases root depth, leaf and grain length, number of panicles, and grain yield in rice. OsFBX257 is a promising breeding target for alleviating drought stress-induced damage in rice.

Mini review: Targeting below-ground plant performance to improve nitrogen use efficiency (NUE) in barley

Nitrogen (N) fertilizer is one of the major inputs for grain crops including barley and its usage is increasing globally. However, N use efficiency (NUE) is low in cereal crops, leading to higher production costs, unfulfilled grain yield potential and environmental hazards. N uptake is initiated from plant root tips but a very limited number of studies have been conducted on roots relevant to NUE specifically. In this review, we used barley, the fourth most important cereal crop, as the primary study plant to investigate this topic. We first highlighted the recent progress and study gaps in genetic analysis results, primarily, the genome-wide association study (GWAS) regarding both biological and statistical considerations. In addition, different factors contributing to NUE are discussed in terms of root morphological and anatomical traits, as well as physiological mechanisms such as N transporter activities and hormonal regulation.

Automated extraction of pod phenotype data from micro-computed tomography

INTRODUCTION

Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.

METHODS

In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape (Brassica napus) mature pods.

RESULTS

With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds.

DISCUSSION

This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

A method of cotton root segmentation based on edge devices

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons

Automating dynamic fine root data collection in the field is a longstanding challenge with multiple applications for co-interpretation and synthesis for ecosystem understanding. High frequency root data are only achievable with paired automated sampling and processing. However, automatic minirhizotron (root camera) instruments are still rare and data are often not collected in natural soils or analysed at high temporal resolution. Instruments must also be affordable for replication and robust under variable natural conditions. Here, we show a system built with off-the-shelf parts which samples at sub-daily resolution. We paired this with a neural network to analyse all images collected. We performed two mesocosm studies and two field trials alongside ancillary data collection (soil CO2 efflux, temperature, and moisture content, and 'PhenoCam'-derived above-ground dynamics). We produce robust and replicated daily time series of root dynamics under all conditions. Temporal root changes were a stronger driver than absolute biomass on soil CO2 efflux in the mesocosm. Proximal sensed above-ground dynamics and below-ground dynamics from minirhizotron data were not synchronized. Root properties extracted were sensitive to soil moisture and occasionally to time of day (potentially relating to soil moisture). This may only affect high frequency imagery and should be considered in interpreting such data.

Effects of high air temperature, drought, and both combinations on maize: A case study

High air temperature (HAT) and natural soil drought (NSD) have seriously affected crop yield and frequently take place in a HAT-NSD combination. Maize (Zea mays) is an important crop, thermophilic but not heat tolerant. In this study, HAT, NSD, and HAT-NSD effects on maize inbred line Huangzao4 -were characterized. Main findings were as follows: H₂O₂ and O^- accumulated much more in immature young leaves than in mature old leaves under the stresses. Lateral roots were highly distributed near the upper pot mix layers under HAT and near root tips under HAT-NSD. Saccharide accumulated mainly in stressed root caps (RC) and formed a highly accumulated saccharide band at the boundary between RC and meristematic zone. Lignin deposition was in stressed roots under NSD and HAT-NSD. Chloroplasts increased in number and formed a high-density ring around leaf vascular bundles (VB) under HAT and HAT-NSD, and sparsely scattered in the peripheral area of VBs under NSD. The RC cells containing starch granules were most under NAD-HAT but least under HAT. Under NSD and HAT-NSD followed by re-watering, anther number per tassel spikelet reduced to 3. These results provide multiple clues for further distinguishing molecular mechanisms for maize to tolerate these stresses.

Genetic dissection of the roles of β-hydroxylases in carotenoid metabolism, photosynthesis, and plant growth in tetraploid wheat (Triticum turgidum L.)

Functional redundancy and subfunctionalization of β-hydroxylases in tetraploid wheat tissues open up opportunities for manipulation of carotenoid metabolism for trait improvement. The genetic diversity provided by subgenome homoeologs in allopolyploid wheat can be leveraged for developing improved wheat varieties with modified chemical traits, including profiles of carotenoids, which play critical roles in photosynthesis, photoprotection, and growth regulation. Carotenoid profiles are greatly influenced by hydroxylation catalyzed by β-hydroxylases (HYDs). To genetically dissect the contribution of HYDs to carotenoid metabolism and wheat growth and yield, we isolated loss-of-function mutants of the two homoeologs of HYD1 (HYD-A1 and HYD-B1) and HYD2 (HYD-A2 and HYD-B2) from the sequenced ethyl methanesulfonate mutant population of the tetraploid wheat cultivar Kronos, and generated various mutant combinations. Although functional redundancy between HYD1 and HYD2 paralogs was observed in leaves, HYD1 homoeologs contributed more than HYD2 homoeologs to carotenoid β-ring hydroxylation in this tissue. By contrast, the HYD2 homoeologs functioned toward production of lutein, the major carotenoid in mature grains, whereas HYD1 homoeologs had a limited role. These results collectively suggested subfunctionalization of HYD genes and homoeologs in different tissues of tetraploid wheat. Despite reduced photoprotective responses observed in the triple hyd-A1 hyd-B1 hyd-A2 and the quadruple hyd-A1 hyd-B1 hyd-A2 hyd-B2 combinatorial mutants, comprehensive plant phenotyping analysis revealed that all mutants analyzed were comparable to the control for growth, yield, and fertility, except for a slight delay in anthesis and senescence as well as accelerated germination in the quadruple mutant. Overall, this research takes steps toward untangling the functions of HYDs in wheat and has implications for improving performance and consumer traits of this economically important global crop.

An image-based technique for automated root disease severity assessment using PlantCV

Premise

Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis.

Methods

Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot.

Results

Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output.

Discussion

Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.

Root system architecture of historical spring wheat cultivars is associated with alleles and transcripts of major functional genes

We evaluated root system architecture (RSA) of a set of 58 historical spring wheat cultivars from Pakistan representing 105 years of selection breeding. The evaluations were carried out under control and water-limited conditions using a high-throughput phenotyping system coupled with RhizoVision Explorer software. The cultivars were classified into three groups based on release year as cultivars released pre-1965, released between 1965 and 2000, and cultivars released post-2000. Under water-limited conditions a decline in 20 out of 25 RSA component traits was observed in pre-1965 cultivars group. Whereas cultivars released after the 1965, so-called green revolution period, showed a decline in 17 traits with significant increments in root length, depth, and steep angle frequency which are important root traits for resource-uptake under water-limited conditions. Similarly, cultivars released after 2000 indicated an increase in the number of roots, depth, diameter, surface area, and steep angle frequency. The coefficient of correlation analysis showed a positive correlation between root depth and yield-related traits under water-limited conditions. We also investigated the effects of green-revolution genes (Rht1) and some phenology-related genes such as DRO1, TaMOR, TaLTPs, TaSus-2B on RSA and identified significant associations of these genes with important root traits. There was strong selection pressure on DRO1 gene in cultivated wheat indicating the allele fixed in modern wheat cultivars is different from landraces. The expression of DRO1, and TaMOR were retrieved from an RNAseq experiment, and results were validated using qRT-PCR. The highest expression of DRO1 and TaMOR was found in Chakwal-50, a rainfed cultivar released in 2008, and MaxiPak-65 released in 1965. We conclude that there is a positive historic change in RSA after 1965 that might be attributed to genetic factors associated with favored RSA traits. Furthermore, we suggest root depth and steep angle as promising traits to withstand water-limited environments and may have implications in selection for breeding.