Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping.

Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.

Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT).

In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession's year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.

Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

Phenotyping for waterlogging tolerance in crops: current trends and future prospects.

Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.

Capturing crop adaptation to abiotic stress using image-based technologies.

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Genetic mapping of the early responses to salt stress in Arabidopsis thaliana.

Salt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single set-up is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from 1 h to 7 days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv' /Fm' ) greatly affected growth maintenance in the early phase of salt stress, whereas the maximum quantum yield (QYmax ) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QYmax and Fv' /Fm' were selected for validation using T-DNA insertion lines. We characterized an unknown protein kinase found in the QYmax locus that reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the candidate genes identified will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses and identifying associations across different traits and time points, which are likely to constitute the genetic components of salinity tolerance.

Assessing Rice Salinity Tolerance: From Phenomics to Association Mapping.

Rice is the most salt-sensitive cereal, suffering yield losses above 50% with soil salinity of 6 dS/m. Thus, understanding the mechanisms of rice salinity tolerance is key to address food security. In this chapter, we provide guidelines to assess rice salinity tolerance using a high-throughput phenotyping platform (HTP) with digital imaging at seedling/early tillering stage and suggest improved analysis methods using stress indices. The protocols described here also include computer scripts for users to improve their experimental design, run genome-wide association studies (GWAS), perform multi-testing corrections, and obtain the Manhattan plots, enabling the identification of loci associated with salinity tolerance. Notably, the computer scripts provided here can be used for any stress or GWAS experiment and independently of HTP.

Dissecting new genetic components of salinity tolerance in two-row spring barley at the vegetative and reproductive stages.

Soil salinity imposes an agricultural and economic burden that may be alleviated by identifying the components of salinity tolerance in barley, a major crop and the most salt tolerant cereal. To improve our understanding of these components, we evaluated a diversity panel of 377 two-row spring barley cultivars during both the vegetative, in a controlled environment, and the reproductive stages, in the field. In the controlled environment, a high-throughput phenotyping platform was used to assess the growth-related traits under both control and saline conditions. In the field, the agronomic traits were measured from plots irrigated with either fresh or saline water. Association mapping for the different components of salinity tolerance enabled us to detect previously known associations, such as HvHKT1;5. Using an "interaction model", which took into account the interaction between treatment (control and salt) and genetic markers, we identified several loci associated with yield components related to salinity tolerance. We also observed that the two developmental stages did not share genetic regions associated with the components of salinity tolerance, suggesting that different mechanisms play distinct roles throughout the barley life cycle. Our association analysis revealed that genetically defined regions containing known flowering genes (Vrn-H3, Vrn-H1, and HvNAM-1) were responsive to salt stress. We identified a salt-responsive locus (7H, 128.35 cM) that was associated with grain number per ear, and suggest a gene encoding a vacuolar H+-translocating pyrophosphatase, HVP1, as a candidate. We also found a new QTL on chromosome 3H (139.22 cM), which was significant for ear number per plant, and a locus on chromosome 2H (141.87 cM), previously identified using a nested association mapping population, which associated with a yield component and interacted with salinity stress. Our study is the first to evaluate a barley diversity panel for salinity stress under both controlled and field conditions, allowing us to identify contributions from new components of salinity tolerance which could be used for marker-assisted selection when breeding for marginal and saline regions.

Genomic history and ecology of the geographic spread of rice.

Rice (Oryza sativa) is one of the world's most important food crops, and is comprised largely of japonica and indica subspecies. Here, we reconstruct the history of rice dispersal in Asia using whole-genome sequences of more than 1,400 landraces, coupled with geographic, environmental, archaeobotanical and paleoclimate data. Originating around 9,000 yr ago in the Yangtze Valley, rice diversified into temperate and tropical japonica rice during a global cooling event about 4,200 yr ago. Soon after, tropical japonica rice reached Southeast Asia, where it rapidly diversified, starting about 2,500 yr BP. The history of indica rice dispersal appears more complicated, moving into China around 2,000 yr BP. We also identify extrinsic factors that influence genome diversity, with temperature being a leading abiotic factor. Reconstructing the dispersal history of rice and its climatic correlates may help identify genetic adaptations associated with the spread of a key domesticated species.

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.

Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species (Solanum pimpinellifolium) through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red-green-blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.

Diverse Traits Contribute to Salinity Tolerance of Wild Tomato Seedlings from the Galapagos Islands.

Traits of modern crops have been heavily selected in agriculture, leaving commercial lines often more susceptible to harsh conditions compared with their wild relatives. Understanding the mechanisms of stress tolerance in wild relatives can enhance crop performance under stress conditions such as high salinity. In this study, we investigated salinity tolerance of two species of wild tomato endemic to the Galapagos Islands, Solanum cheesmaniae and Solanum galapagense Since these tomatoes grow well despite being constantly splashed with seawater, they represent a valuable genetic resource for improving salinity tolerance in commercial tomatoes. To explore their potential, we recorded over 20 traits reflecting plant growth, physiology, and ion content in 67 accessions and two commercial tomato lines of Solanum lycopersicum. Salt treatments were applied for 10 d using supported hydroponics. The Galapagos tomatoes displayed greater tolerance to salt stress than the commercial lines and showed substantial natural variation in their responses. The accessions LA0317, LA1449, and LA1403 showed particularly high salinity tolerance based on growth under salinity stress. Therefore, Galapagos tomatoes should be further explored to identify the genes underlying their high tolerance and be used as a resource for increasing the salinity tolerance of commercial tomatoes. The generated data, along with useful analysis tools, have been packaged and made publicly available via an interactive online application (https://mmjulkowska.shinyapps.io/La_isla_de_tomato/) to facilitate trait selection and the use of Galapagos tomatoes for the development of salt-tolerant commercial tomatoes.

Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress.

With salt stress presenting a major threat to global food production, attention has turned to the identification and breeding of crop cultivars with improved salt tolerance. For instance, some accessions of wild species with higher salt tolerance than commercial varieties are being investigated for their potential to expand food production into marginal areas or to use brackish waters for irrigation. However, assessment of individual plant responses to salt stress in field trials is time-consuming, limiting, for example, longitudinal assessment of large numbers of plants. Developments in Unmanned Aerial Vehicle (UAV) sensing technologies provide a means for extensive, repeated and consistent phenotyping and have significant advantages over standard approaches. In this study, 199 accessions of the wild tomato species, Solanum pimpinellifolium, were evaluated through a field assessment of 600 control and 600 salt-treated plants. UAV imagery was used to: (1) delineate tomato plants from a time-series of eight RGB and two multi-spectral datasets, using an automated object-based image analysis approach; (2) assess four traits, i.e., plant area, growth rates, condition and Plant Projective Cover (PPC) over the growing season; and (3) use the mapped traits to identify the best-performing accessions in terms of yield and salt tolerance. For the first five campaigns, >99% of all tomato plants were automatically detected. The omission rate increased to 2-5% for the last three campaigns because of the presence of dead and senescent plants. Salt-treated plants exhibited a significantly smaller plant area (average control and salt-treated plant areas of 0.55 and 0.29 m2, respectively), maximum growth rate (daily maximum growth rate of control and salt-treated plant of 0.034 and 0.013 m2, respectively) and PPC (5-16% difference) relative to control plants. Using mapped plant condition, area, growth rate and PPC, we show that it was possible to identify eight out of the top 10 highest yielding accessions and that only five accessions produced high yield under both treatments. Apart from showcasing multi-temporal UAV-based phenotyping capabilities for the assessment of plant performance, this research has implications for agronomic studies of plant salt tolerance and for optimizing agricultural production under saline conditions.

High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare).

To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.

Salt stress under the scalpel - dissecting the genetics of salt tolerance.

Salt stress limits the productivity of crops grown under saline conditions, leading to substantial losses of yield in saline soils and under brackish and saline irrigation. Salt tolerant crops could alleviate these losses while both increasing irrigation opportunities and reducing agricultural demands on dwindling freshwater resources. However, despite significant efforts, progress towards this goal has been limited, largely because of the genetic complexity of salt tolerance for agronomically important yield-related traits. Consequently, the focus is shifting to the study of traits that contribute to overall tolerance, thus breaking down salt tolerance into components that are more genetically tractable. Greater consideration of the plasticity of salt tolerance mechanisms throughout development and across environmental conditions furthers this dissection. The demand for more sophisticated and comprehensive methodologies is being met by parallel advances in high-throughput phenotyping and sequencing technologies that are enabling the multivariate characterisation of vast germplasm resources. Alongside steady improvements in statistical genetics models, forward genetics approaches for elucidating salt tolerance mechanisms are gaining momentum. Subsequent quantitative trait locus and gene validation has also become more accessible, most recently through advanced techniques in molecular biology and genomic analysis, facilitating the translation of findings to the field. Besides fuelling the improvement of established crop species, this progress also facilitates the domestication of naturally salt tolerant orphan crops. Taken together, these advances herald a promising era of discovery for research into the genetics of salt tolerance in plants.

The Genome Sequence of the Wild Tomato Solanum pimpinellifolium Provides Insights Into Salinity Tolerance.

Solanum pimpinellifolium, a wild relative of cultivated tomato, offers a wealth of breeding potential for desirable traits such as tolerance to abiotic and biotic stresses. Here, we report the genome assembly and annotation of S. pimpinellifolium 'LA0480.' Moreover, we present phenotypic data from one field experiment that demonstrate a greater salinity tolerance for fruit- and yield-related traits in S. pimpinellifolium compared with cultivated tomato. The 'LA0480' genome assembly size (811 Mb) and the number of annotated genes (25,970) are within the range observed for other sequenced tomato species. We developed and utilized the Dragon Eukaryotic Analyses Platform (DEAP) to functionally annotate the 'LA0480' protein-coding genes. Additionally, we used DEAP to compare protein function between S. pimpinellifolium and cultivated tomato. Our data suggest enrichment in genes involved in biotic and abiotic stress responses. To understand the genomic basis for these differences in S. pimpinellifolium and S. lycopersicum, we analyzed 15 genes that have previously been shown to mediate salinity tolerance in plants. We show that S. pimpinellifolium has a higher copy number of the inositol-3-phosphate synthase and phosphatase genes, which are both key enzymes in the production of inositol and its derivatives. Moreover, our analysis indicates that changes occurring in the inositol phosphate pathway may contribute to the observed higher salinity tolerance in 'LA0480.' Altogether, our work provides essential resources to understand and unlock the genetic and breeding potential of S. pimpinellifolium, and to discover the genomic basis underlying its environmental robustness.

Environmental stress is the major cause of transcriptomic and proteomic changes in GM and non-GM plants.

The approval of genetically modified (GM) crops is preceded by years of intensive research to demonstrate safety to humans and environment. We recently showed that in vitro culture stress is the major factor influencing proteomic differences of GM vs. non-GM plants. This made us question the number of generations needed to erase such "memory". We also wondered about the relevance of alterations promoted by transgenesis as compared to environment-induced ones. Here we followed three rice lines (1-control, 1-transgenic and 1-negative segregant) throughout eight generations after transgenesis combining proteomics and transcriptomics, and further analyzed their response to salinity stress on the F6 generation. Our results show that: (a) differences promoted during genetic modification are mainly short-term physiological changes, attenuating throughout generations, and (b) environmental stress may cause far more proteomic/transcriptomic alterations than transgenesis. Based on our data, we question what is really relevant in risk assessment design for GM food crops.

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 dictionaries and combination of these terms. To illustrate the utility of DES-TOMATO, we provide several examples how one can efficiently use this KB to retrieve known or potentially novel information. DES-TOMATO is free for academic and nonprofit users and can be accessed at http://cbrc.kaust.edu.sa/des_tomato/, using any of the mainstream web browsers, including Firefox, Safari and Chrome.

Corrigendum: The genome of Chenopodium quinoa.

This corrects the article DOI: 10.1038/nature21370.