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Background: Precision phenotyping of short-term transpiration response to environmental conditions and transpiration patterns throughout wheat development enables a better understanding of specific trait compositions that lead to improved transpiration efficiency. Transpiration and related traits were evaluated in a set of 79 winter wheat lines using the custom-built "DroughtSpotter XXL" facility. The 120 l plant growth containers implemented in this phenotyping platform enable gravimetric quantification of water use in real-time under semi-controlled, yet field-like conditions across the entire crop life cycle. Results: The resulting high-resolution data enabled identification of significant developmental stage-specific variation for genotype rankings in transpiration efficiency. In addition, for all examined genotypes we identified the genotype-specific breakpoint in transpiration in response to increasing vapour pressure deficit, with breakpoints ranging between 2.75 and 4.1 kPa. Conclusion: Continuous monitoring of transpiration efficiency and diurnal transpiration patterns enables identification of hidden, heritable genotypic variation for transpiration traits relevant for wheat under drought stress. Since the unique experimental setup mimics field-like growth conditions, the results of this study have good transferability to field conditions. [ABSTRACT FROM AUTHOR]

SUMMARY: Plant hormones are chemical signals governing almost every aspect of a plant's life cycle and responses to environmental cues. They are enmeshed within complex signaling networks that can only be deciphered by using broad‐scale analytical methods to capture information about several plant hormone classes simultaneously. Methods used for this purpose are all based on reversed‐phase (RP) liquid chromatography and mass spectrometric detection. Hydrophilic interaction chromatography (HILIC) is an alternative chromatographic method that performs well in analyses of biological samples. We therefore developed and validated a HILIC method for broad‐scale plant hormone analysis including a rapid sample preparation procedure; moreover, derivatization or fractionation is not required. The method enables plant hormone screening focused on polar and moderately polar analytes including cytokinins, auxins, jasmonates, abscisic acid and its metabolites, salicylates, indoleamines (melatonin), and 1‐aminocyclopropane‐1‐carboxylic acid (ACC), for a total of 45 analytes. Importantly, the major pitfalls of ACC analysis have been addressed. Furthermore, HILIC provides orthogonal selectivity to conventional RP methods and displays greater sensitivity, resulting in lower limits of quantification. However, it is less robust, so procedures to increase its reproducibility were established. The method's potential is demonstrated in a case study by employing an approach combining hormonal analysis with phenomics to examine responses of three Arabidopsis ecotypes toward three abiotic stress treatments: salinity, low nutrient availability, and their combination. The case study showcases the value of the simultaneous determination of several plant hormone classes coupled with phenomics data when unraveling processes involving complex cross‐talk under diverse plant‐environment interactions. [ABSTRACT FROM AUTHOR]

Nitrogen use efficiency (NUE) is important for the growth and development of rice and is significant in reducing the costs of rice production. OsNRT1.1b is involved in nitrate assimilation, and the alleles at position 21,759,092 on chromosome 10 clearly separate indica (Pathum Thani 1 (PTT1) and Homcholasit (HCS)) and japonica (Azucena and Leum Pua (LP)) rice varieties. Rice morphological and physiological traits were collected at three nitrogen levels (N0 = 0 kg ha−1, N7 = 43.75 kg ha−1, and N14 = 87.5 kg ha−1). Leaf and tiller numbers in PTT1 and HCS at N7 and N14 were two to three times higher than those at N0. At harvest, the biomass yield in PTT1 was the highest, while the total grain number in HCS was the maximum. The leaf widths and total chlorophyll contents (SPAD units) of Azucena and LP increased with nitrogen application as well as photosynthetic pigment parameters; for example, plant senescence reflectance indices (PSRIs), structure-insensitive pigment indices (SIPIs), and modified chlorophyll absorption ratio indices (MCARIs) were highly related in the japonica varieties. PTT1 and HCS, both carrying the A allele at OsNRT1.1b, had better NUE than Azucena and LP with the G allele. HCS, overall, had better NUE than PTT1. The translation to grain yield of assimilates was remarkable in PTT1 and HCS compared with Azucena and LP. In addition, HCS converted biomass for a 75% higher yield than PTT1. The ability of HCS to produce high yields was achieved even at N7 nitrogen fertilization, manifesting efficient use of nitrogen. [ABSTRACT FROM AUTHOR]

Introduction: Although drought resistance of a plant may be achieved through morphological, structural, physiological, cellular, and molecular adaptations, most studies remain limited to quantifying the effect of drought on biomass. Methods: Using a high throughput phenotypic imaging system, we evaluated the drought resistance of 151 bean accessions (Phaseolinae; Fabaceae) in an explorative approach, by quantifying five different traits simultaneously: biomass, water use efficiency (WUE), relative water content (RWC), chlorophyll content (NDVI), and root/shoot ratio. Since crop wild relatives are important resources for breeding programs, we analyzed both wild and cultivated accessions, most of which have never been evaluated for drought resistance before. Results: We demonstrate that the five traits are affected very differently by drought in the studied accessions, with significant correlations existing only between the biomass and WUE indicators (r=0.39), and between the RWC and NDVI indicators (r=0.40). When grouping accessions by subgenus or by species, large intraspecific and within subgenus variation was found. For this reason, we performed a cluster analysis, which grouped the accessions into five distinct clusters with similar response profiles. We also correlated the drought resistance for each accession to local climate variables at their original collection sites. The biomass, WUE, and RWC indicators were significantly correlated to annual precipitation (r=0.40, r=0.20, r=0.22, respectively), confirming that accessions from arid environments are generally more drought resistant. Discussion: Our results demonstrate that the drought resistance of Phaseolinae beans is a multifaceted characteristic and cannot be simply quantified through biomass. Furthermore, the broader knowledge of the drought resistance of the accessions studied heremay prove an invaluable resource for future crop production. [ABSTRACT FROM AUTHOR]

Over the last 10 years, global raspberry production has increased by 47.89%, based mainly on the red raspberry species (Rubus idaeus). However, the black raspberry (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late leaf rust in raspberries. For that, we used an interspecific multiparental population using the species mentioned above as parents, 2 different statistical approaches to associate the phenotypes with markers [GWAS (genome-wide association studies) and copula graphical models], and 2 phenotyping methodologies from the first to the 17th day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect, at position 13.3 Mb on chromosome 5, possibly controls late leaf rust resistance, as both GWAS and the network suggested the same marker. Of the 12 genes flanking its region, 4 were possible receptors, 3 were likely defense executors, 1 gene was likely part of signaling cascades, and 4 were classified as nondefense related. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different candidate regions, potentially complementing the genetic control comprehension. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56–0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes. [ABSTRACT FROM AUTHOR]

SUMMARY: 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. Significance Statement: While wild tomato relative, Solanum pimpinellifolium, has been previously used for agronomic improvements of cultivated tomato, its contributions in abiotic stress tolerance remain underexplored. Here, we developed a well genotyped diversity panel of S. pimpinellifolium and screened it under greenhouse and field conditions for salt stress tolerance. This study provides genetic resources, as well as initial identification of accessions and genetic loci underlying salt tolerance in wild tomato. [ABSTRACT FROM AUTHOR]

Abstract Background Precision phenotyping of short-term transpiration response to environmental conditions and transpiration patterns throughout wheat development enables a better understanding of specific trait compositions that lead to improved transpiration efficiency. Transpiration and related traits were evaluated in a set of 79 winter wheat lines using the custom-built “DroughtSpotter XXL” facility. The 120 l plant growth containers implemented in this phenotyping platform enable gravimetric quantification of water use in real-time under semi-controlled, yet field-like conditions across the entire crop life cycle. Results The resulting high-resolution data enabled identification of significant developmental stage-specific variation for genotype rankings in transpiration efficiency. In addition, for all examined genotypes we identified the genotype-specific breakpoint in transpiration in response to increasing vapour pressure deficit, with breakpoints ranging between 2.75 and 4.1 kPa. Conclusion Continuous monitoring of transpiration efficiency and diurnal transpiration patterns enables identification of hidden, heritable genotypic variation for transpiration traits relevant for wheat under drought stress. Since the unique experimental setup mimics field-like growth conditions, the results of this study have good transferability to field conditions.

Abstract 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.

Advances in informatics, robotics, and imaging techniques make it possible to use state-of-the-art digital reconstruction technologies for high-throughput plant phenotyping (HTPP) affected by stress factors, as well as for the ontology of their structural and functional traits. Digital imaging of structural and functional features of the aboveground part of plants is non-destructive and plants can be monitored throughout their entire life cycle. In the experiment with tomato plants (Solanum lycopersicum L.; cv. Gruzanski zlatni) grown in controlled environmental conditions and affected by gradual soil dehydration, we evaluated phenotypic traits and phenotypic plasticity by the PlantScreenTM platform using digital imaging of plant optical signals. In this study, 25 different morpho-physiological traits of the plant were evaluated during the precise control and monitoring of the water content in the soil. Different levels of plant water supply induced statistically significant differences in the formation of individual phenotypic traits. Several plant traits have been identified that are characterized by low variability in both well-hydrated and water-stressed conditions, as well as traits with high phenotypic plasticity. Geometric traits (especially Isotop, Round-2top, and Compside) showed a relatively low level of drought-induced phenotypic plasticity. However, functional and chemometric characteristics (ΔF/F′m, Rfd, Water-1, and ARI-1) showed the potential to exhibit rapid plasticity in water-stressed conditions. Our results confirmed that a high-throughput phenotyping methodology coupled with advanced statistical analysis tools can be successfully applied to characterize crop stress responses and identify traits associated with crop stress tolerance.

Cercospora leaf spot (CLS) is the most destructive fungal disease, deteriorating the production and productivity of mungbean (Vigna radiata (L.) Wilczek). Mungbean is one of the most nutritionally and environmentally important legumes, with popularity currently increasing as a ‘future smart food crop’ due to its several health benefits. In recent years, there has been considerable research progress in improving disease resistance in legumes. However, only a limited number of studies have pinpointed potential genes and candidate genes associated with resistance traits to CLS in mungbeans. Identifying the potential resistant resources through combined omics approaches is an efficient strategy to screen the best Cercospora-resistant mungbean varieties for further molecular breeding and improvement. Potential omics approaches are important tools to predict disease management strategies, alleviate chemical overuse, and mitigate problems due to malnutrition. Sustainable breeding research efforts using potential combined omics, including automated phenotyping, to promote important resistant traits associated with CLS in mungbeans are still unexplored and a key issue that needs to be addressed. Omics-technology-based research findings on resistance genes, proteins, and metabolites against CLS in mungbean are recognised in this review. Due to a limitation of research findings specifically underscoring the use of omics tools for screening resistant mungbean against CLS, best related research outcomes on other crops are included in this review.

Abstract Non-photochemical quenching (NPQ) is a protective mechanism for dissipating excess energy generated during photosynthesis in the form of heat. The accelerated relaxation of the NPQ in fluctuating light can lead to an increase in the yield and dry matter productivity of crops. Since the measurement of NPQ is time-consuming and requires specific light conditions, theoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping. We investigated the potential of NPQ(T) to be used for testing plant genetic resources of chickpea under drought stress with non-invasive High-throughput Phenotyping complemented with yield traits. Besides a high correlation between the hundred-seed-weight and the Estimated Biovolume, significant differences were observed between the two types of chickpea desi and kabuli for Estimated Biovolume and NPQ(T). Desi was able to maintain the Estimated Biovolume significantly better under drought stress. One reason could be the effective dissipation of excess excitation energy in photosystem II, which can be efficiently measured as NPQ(T). Screening of plant genetic resources for photosynthetic performance could take pre-breeding to a higher level and can be implemented in a variety of studies, such as here with drought stress or under fluctuating light in a High-throughput Phenotyping manner using NPQ(T).

Societal Impact Statement Summary Pulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality.Precision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP. [ABSTRACT FROM AUTHOR]

SUMMARY: Stem is important for assimilating transport and plant strength; however, less is known about the genetic basis of its structural characteristics. In this study, a high‐throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles, which allows the establishment of a stem cross‐section phenotype dataset containing anatomical information of 1738 images from hand‐cut transections of stems collected from 387 rice germplasm accessions grown over two successive seasons. Then, the phenotypic diversity of the rice accessions was evaluated. Genome‐wide association studies identified 94, 83, and 66 significant single nucleotide polymorphisms (SNPs) for the assayed traits in 2 years and their best linear unbiased estimates, respectively. These SNPs can be integrated into 29 quantitative trait loci (QTL), and 11 of them were common in 2 years, while correlated traits shared 19. In addition, 173 candidate genes were identified, and six located at significant SNPs were repeatedly detected and annotated with a potential function in stem development. By using three introgression lines (chromosome segment substitution lines), four of the 29 QTLs were validated. LOC_Os01g70200, located on the QTL uq1.4, is detected for the area of small vascular bundles (SVB) and the rate of large vascular bundles number to SVB number. Besides, the CRISPR/Cas9 editing approach has elucidated the function of the candidate gene LOC_Os06g46340 in stem development. In conclusion, the results present a time‐ and cost‐effective method that provides convenience for extracting rice stem anatomical traits and the candidate genes/QTL, which would help improve rice. [ABSTRACT FROM AUTHOR]

Recent advancements in high-throughput phenotyping have led to the use of drones with RGB sensors for evaluating plant traits. This study explored the relationships between vegetation indices (VIs) with grain yield and morphoagronomic and physiological traits in maize genotypes. Eight maize hybrids, including those from the UENF breeding program and commercial varieties, were evaluated using a randomized block design with four replications. VIs were obtained at various stages using drones and Pix4D Mapper 4.7.5 software. Analysis revealed significant differences in morphoagronomic traits and photosynthetic capacity. At 119 days after planting (DAP), the RGB vegetation index VARI showed a significant correlation (r = 0.99) with grain yield. VARI also correlated with female flowering (r = −0.87), plant height (r = −0.79), 100-grain weight (r = −0.77), and anthocyanin concentration (r = −0.86). PCA showed a clear separation between local and commercial hybrids, explaining 46.7% of variance at 91 DAP, 52.3% at 98 DAP, 64.2% at 112 DAP, and 66.1% at 119 DAP. This study highlights the utility of VIs in maize phenotyping and genotype selection during advanced reproductive stages. [ABSTRACT FROM AUTHOR]

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. [ABSTRACT FROM AUTHOR]

Drought is a major factor affecting crops, thus efforts are needed to increase plant resilience to this abiotic stress. The overlapping signaling pathways between drought and cell wall integrity maintenance responses create a possibility of increasing drought resistance by modifying cell walls. Here, using herbaceous and woody plant model species, Arabidopsis and hybrid aspen, respectively, we investigated how the integrity of xylan in secondary walls affects the responses of plants to drought stress. Plants, in which secondary wall xylan integrity was reduced by expressing fungal GH10 and GH11 xylanases or by affecting genes involved in xylan backbone biosynthesis, were subjected to controlled drought while their physiological responses were continuously monitored by RGB, fluorescence, and/or hyperspectral cameras. For Arabidopsis, this was supplemented with survival test after complete water withdrawal and analyses of stomatal function and stem conductivity. All Arabidopsis xylan-impaired lines showed better survival upon complete watering withdrawal, increased stomatal density and delayed growth inhibition by moderate drought, indicating increased resilience to moderate drought associated with modified xylan integrity. Subtle differences were recorded between xylan biosynthesis mutants (irx9, irx10 and irx14) and xylanaseexpressing lines. irx14 was the most drought resistant genotype, and the only genotype with increased lignin content and unaltered xylem conductivity despite its irx phenotype. Rosette growth was more affected by drought in GH11- than in GH10-expressing plants. In aspen, mild downregulation of GT43B and C genes did not affect drought responses and the transgenic plants grew better than the wild-type in drought and well-watered conditions. Both GH10 and GH11 xylanases strongly inhibited stem elongation and root growth in well-watered conditions but growth was less inhibited by drought in GH11-expressing plants than in wild-type. Overall, plants with xylan integrity impairment in secondary walls were less affected than wild-type by moderately reduced water availability but their responses also varied among genotypes and species. Thus, modifying the secondary cell wall integrity can be considered as a potential strategy for developing crops better suited to withstand water scarcity, but more research is needed to address the underlying molecular causes of this variability. [ABSTRACT FROM AUTHOR]

A low-input-based farming system can reduce the adverse effects of modern agriculture through proper utilization of natural resources. Modern varieties often need to improve in low-input settings since they are not adapted to these systems. In addition, rice is one of the most widely cultivated crops worldwide. Enhancing rice performance under a low input system will significantly reduce the environmental concerns related to rice cultivation. Traits that help rice to maintain yield performance under minimum inputs like seedling vigor, appropriate root architecture for nutrient use efficiency should be incorporated into varieties for low input systems through integrated breeding approaches. Genes or QTLs controlling nutrient uptake, nutrient assimilation, nutrient remobilization, and root morphology need to be properly incorporated into the rice breeding pipeline. Also, genes/QTLs controlling suitable rice cultivars for sustainable farming. Since several variables influence performance under low input conditions, conventional breeding techniques make it challenging to work on many traits. However, recent advances in omics technologies have created enormous opportunities for rapidly improving multiple characteristics. This review highlights current research on features pertinent to low-input agriculture and provides an overview of alternative genomics-based breeding strategies for enhancing genetic gain in rice suitable for low-input farming practices. [ABSTRACT FROM AUTHOR]

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs. [ABSTRACT FROM AUTHOR]

To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as "pot binding", particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants. [ABSTRACT FROM AUTHOR]

Non-photochemical quenching (NPQ) is a protective mechanism for dissipating excess energy generated during photosynthesis in the form of heat. The accelerated relaxation of the NPQ in fluctuating light can lead to an increase in the yield and dry matter productivity of crops. Since the measurement of NPQ is time-consuming and requires specific light conditions, theoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping. We investigated the potential of NPQ(T) to be used for testing plant genetic resources of chickpea under drought stress with non-invasive High-throughput Phenotyping complemented with yield traits. Besides a high correlation between the hundred-seed-weight and the Estimated Biovolume, significant differences were observed between the two types of chickpea desi and kabuli for Estimated Biovolume and NPQ(T). Desi was able to maintain the Estimated Biovolume significantly better under drought stress. One reason could be the effective dissipation of excess excitation energy in photosystem II, which can be efficiently measured as NPQ(T). Screening of plant genetic resources for photosynthetic performance could take pre-breeding to a higher level and can be implemented in a variety of studies, such as here with drought stress or under fluctuating light in a High-throughput Phenotyping manner using NPQ(T). [ABSTRACT FROM AUTHOR]

BACKGROUND: Determinants of culinary qualities of East African highland cooking bananas (EAHCB) are not well known. This constrains the inclusion of user‐preferred traits in breeding. The present study aimed to quantify key indicators of user‐preferred characteristics to enable selection of acceptable hybrids. RESULTS: Qualitative characteristics that drive preference were big bunches (15–34 kg), long straight/slightly curved fingers (12–23 cm), yellowness and soft texture. Descriptive sensory analysis of the intensity of colour and texture the 23 genotypes revealed that landraces Kibuzi, Mbwazirume, Nakitembe and Mpologoma had higher intensity of yellowness and lower intensity of hardness (softer) and a low score (≤ 1.0) of astringency taste. A preference test showed that they had higher acceptability scores. Biochemical, instrumental and sensory data revealed correlations between sensory firmness and instrumental hardness (r = 0.5), sensory firmness and amylopectin (r = −0.54), suggesting that qualitative descriptions can be predicted by instrumental and biochemical indicators. Significant (P < 0.05) variations in amylose and total starch content were observed in different varieties. Moderate correlations between instrumental hardness and firmness in mouth (r = 0.55), cohesiveness and firmness in the mouth (r = 0.57), and adhesiveness and firmness in the mouth (r = 0.64) were observed. Surprisingly, carotenoids content was not correlated with yellowness in cooked matooke. However, positive correlations were observed between chroma (b*) parameters of raw matooke and sensorial assessed color on cooked samples. CONCLUSION: Qualitative characteristis; the bunch, pulp colour and texture; that drive users‐preference in the EAHCB were quantified, paving way for breeders to use them to select genotypes with these attributes early in the breeding process. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light‐scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open‐source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 μm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open‐source pipeline offers an accurate, robust and discriminating solution for medium‐throughput phenotyping of yam starch granule size distribution and shape classification. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: The assessment of user acceptability in relation to crop quality traits should be a full part of breeding selection programs. Our methodology is based on a combination of sensory approaches aiming to evaluate the sensory characteristics and user acceptability of root, tuber and banana (RTB) varieties. RESULTS: The four‐stepped approach links sensory characteristics to physicochemical properties and end‐user acceptance. It starts with the development of key quality traits using qualitative approaches (surveys and ranking) and it applies a range of sensory tests such as Quantitative Descriptive Analysis with a trained panel, Check‐All‐That‐apply, nine‐point hedonic scale and Just‐About‐Right with consumers. Results obtained on the same samples from the consumer acceptance, sensory testing and physicochemical testing are combined to explore correlations and develop acceptability thresholds. CONCLUSION: A combined qualitative and quantitative approach involving different sensory techniques is necessary to capture sensory acceptance of products from new RTB clones. Some sensory traits can be correlated with physicochemical characteristics and could be evaluated using laboratory instruments (e.g. texture). Other traits (e.g. aroma and mealiness) are more difficult to predict, and the use of a sensory panel is still necessary. For these latter traits, more advanced physicochemical methods that could accelerate the breeding selection through high throughput phenotyping are still to be developed. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: Variability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High‐throughput phenotyping is required to breed varieties that satisfy consumer preferences. Results: Near‐infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R2c) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross‐validation (R2cv) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R2c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross‐validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r2) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross‐validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population. Conclusions: NIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Boiled yam key quality attributes typical for West African consumers are that it is crumbly, easy to break and has a sweet taste. New yam varieties are being developed but high‐ or medium‐throughput tools to assess the required quality traits and their range of acceptance are limited. This study assessed the acceptance thresholds of these quality attributes and established predictive models for screening yam varieties that meet the required consumer preferences. RESULTS: Overall liking was associated with sweet taste, crumbliness and easiness to break (r‐values 0.502, 0.291 and −0.087, respectively). These parameters and selected biophysical parameters highly discriminated the boiled yam varieties. Crumbly texture and easiness to break were well predicted by penetration force and dry matter, whereas sweet taste were well predicted by dry matter and sugar intensity. A high crumbliness and sweet taste are preferred (sensory scores above 6.19 and 6.22 for crumbly and sweet taste, respectively, on a 10 cm unstructured line scale), while a too high easiness to break is disliked (sensory scores ranging from 4.72 to 7.62). Desirable biophysical targets were between 5.1 and 7.1 N for penetration force, dry matter around 39% and sugar intensity below 3.62 g 100 g−1. Some improved varieties fulfilled the acceptable thresholds, and screening was improved through deviation from the optimum. CONCLUSION: Acceptance thresholds and deviation from optimum for boiled yam assessed through the instrumental measurements are promising tools for yam breeders. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Cercospora leaf spot (CLS) is the most destructive fungal disease, deteriorating the production and productivity of mungbean (Vigna radiata (L.) Wilczek). Mungbean is one of the most nutritionally and environmentally important legumes, with popularity currently increasing as a 'future smart food crop' due to its several health benefits. In recent years, there has been considerable research progress in improving disease resistance in legumes. However, only a limited number of studies have pinpointed potential genes and candidate genes associated with resistance traits to CLS in mungbeans. Identifying the potential resistant resources through combined omics approaches is an efficient strategy to screen the best Cercospora-resistant mungbean varieties for further molecular breeding and improvement. Potential omics approaches are important tools to predict disease management strategies, alleviate chemical overuse, and mitigate problems due to malnutrition. Sustainable breeding research efforts using potential combined omics, including automated phenotyping, to promote important resistant traits associated with CLS in mungbeans are still unexplored and a key issue that needs to be addressed. Omics-technology-based research findings on resistance genes, proteins, and metabolites against CLS in mungbean are recognised in this review. Due to a limitation of research findings specifically underscoring the use of omics tools for screening resistant mungbean against CLS, best related research outcomes on other crops are included in this review. [ABSTRACT FROM AUTHOR]

A major issue in several farming areas of the Mediterranean basin consists of drought and salinity stress. This stress is mainly due to a steady exposition of warm daily temperature and heatwaves, moreover with inevitable irrigation with saline water. Therefore, detecting the stress is essential to minimise significant yield loss and preserve agricultural sustainability. In this context, remote and proximal sensing can play a crucial role in allowing fast, not destructive, extensive, and reliable assessment of crop status. In this work, the effectiveness of several multispectral indices in detecting salinity and water stress in tomato plants, grown under controlled green-house conditions, was investigated. Three different classifiers (fine tree model, linear discriminant model, and linear support vector machines model) were used to verify whether, and the extent to which, the adopted multispectral indices can be adopted to identify a stress condition of the tomato plants. In the experimental campaign, the stress occurrence on tomato plants was assessed on the base of a set of ecophysiological measurements, such as transpiration, stomatal conductance, and photosynthesis rate. Obtained results showed that a classification model based on linear support vector machines, exploiting the combination of Photochemical Reflectance Index and the Chlorophyl Index, can detect drought and salinity stress in tomato plants with an accuracy higher than 94%.

Near-infrared spectroscopy (NIRS) provides a high-throughput phenotyping technique to assist breeding for improved faba bean seed quality. We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models. Protein was the most reliably predicted trait (R2 = 0.96–0.98) across field trials, followed by oil (R2 = 0.82–0.86) and oleic acid (R2 = 0.31–0.68). Samples for training the models were selected using K-means clustering. The optimal statistical approach for prediction was compound-specific: PLS for protein (Root Mean Squared Error - RMSE = 0.46), BB for oil (RMSE = 0.067), and EN for oleic acid content (RMSE = 2.83). Reduced training set simulations revealed different effects on prediction accuracy depending on the model and compound. Several NIR regions were pinpointed as highly informative for the compounds, using the shrinkage and variable selection capabilities of EN and BB.

IntroductionAlthough drought resistance of a plant may be achieved through morphological, structural, physiological, cellular, and molecular adaptations, most studies remain limited to quantifying the effect of drought on biomass.MethodsUsing a highthroughput phenotypic imaging system, we evaluated the drought resistance of 151 bean accessions (Phaseolinae; Fabaceae) in an explorative approach, by quantifying five different traits simultaneously: biomass, water use efficiency (WUE), relative water content (RWC), chlorophyll content (NDVI), and root/shoot ratio. Since crop wild relatives are important resources for breeding programs, we analyzed both wild and cultivated accessions, most of which have never been evaluated for drought resistance before. ResultsWe demonstrate that the five traits are affected very differently by drought in the studied accessions, with significant correlations existing only between the biomass and WUE indicators (r=0.39), and between the RWC and NDVI indicators (r=0.40). When grouping accessions by subgenus or by species, large intraspecific and withinsubgenus variation was found. For this reason, we performed a cluster analysis, which grouped the accessions into five distinct clusters with similar response profiles. We also correlated the drought resistance for each accession to local climate variables at their original collection sites. The biomass, WUE, and RWC indicators were significantly correlated to annual precipitation (r=0.40, r=0.20, r=0.22, respectively), confirming that accessions from arid environments are generally more drought resistant.DiscussionOur results demonstrate that the drought resistance of Phaseolinae beans is a multifaceted characteristic and cannot be simply quantified through biomass. Furthermore, the broader knowledge of the drought resistance of the accessions studied here may prove an invaluable resource for future crop production.

Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a 2-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20,843 genes. MegaBayesC selected 15 single nucleotide polymorphisms as important for flowering time, with 13 located within 100 kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.

BackgroundRemote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).ResultsWe demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.ConclusionsTSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R2 was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

Understanding cultivars' physiological traits variations under abiotic stresses is critical to improve phenotyping and selections of resistant crop varieties. Traditional methods of accessing physiological traits in plants are costly and time consuming, which prevents their use in breeding programs. Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing phenotyping several genotypes in a high-throughput manner. We collected spectroscopy data of twenty soybean cultivars planted under well-watered and drought conditions during the reproductive phase. At 20 days after drought was imposed, we measured leaf pigments content (chlorophyll a and b, and carotenoids), specific leaf area, electrons transfer rate, and photosynthetic active radiation. At 28 days after drought imposition, we measured leaf pigments content, specific leaf area, relative water content, and leaf temperature. Partial least square regression models accurately predicted leaf pigments content, specific leaf area, and leaf temperature (cross-validation R2 ranging from 0.56 to 0.84). Discriminant analysis using 54 wavelengths was able to select the best-performance cultivars regarding all evaluated physiological traits. We showed the great potential of using spectroscopy as a feasible, non-destructive, and accurate method to estimate physiological traits and screening of superior genotypes.

Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.

Given the difficulties in accessing plant roots in situ , high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement. [ABSTRACT FROM AUTHOR]

Summary: Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques.In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high‐throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri.Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green–Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non‐modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids).By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant–environment interactions and have implications for crop improvement strategies. [ABSTRACT FROM AUTHOR]

SUMMARY: The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21‐0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone. Significance Statement: 149 accessions of Brachypodium were phenotyped across time under control, drought, heat, and the combination of drought and heat. Unexpectedly, heat alone resulted in the most amount of tissue damage in comparison to drought alone or the combined stress treatment. Climate of origin was significantly correlated with changes in height and plant tissue damage under stress and several genetic loci were also found to be associated with phenotypic changes under stress. [ABSTRACT FROM AUTHOR]

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype ( r y , y ^ ) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was −0.17, with DMIp was 0.40 and with EBp was −0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance. [ABSTRACT FROM AUTHOR]

The LeafArea package is an innovative tool for estimating leaf area in six Andean fruit species, utilizing leaf length and width along with species type for accurate predictions. This research highlights the package's integration of advanced machine learning algorithms, including GLM, GLMM, Random Forest, and XGBoost, which excels in predictive accuracy. XGBoost's superior performance is evident in its low prediction errors and high R2 value, showcasing the effectiveness of machine learning in leaf area estimation. The LeafArea package, thus, offers significant contributions to the study of plant growth dynamics, providing researchers with a robust and precise tool for informed decision making in resource allocation and crop management. Dataset: Velasquez-Vasconez et al. (2023). Photographs of leaves from seven plant species including S. quitoense, S. betaceum, P. peruviana, R. glaucus, P. ligularis, P. vulgaris, and P. edulis figshare. Figure. https://doi.org/10.6084/m9.figshare.24618183. Dataset License: CC-BY. [ABSTRACT FROM AUTHOR]

A low-input-based farming system can reduce the adverse effects of modern agriculture through proper utilization of natural resources. Modern varieties often need to improve in low-input settings since they are not adapted to these systems. In addition, rice is one of the most widely cultivated crops worldwide. Enhancing rice performance under a low input system will significantly reduce the environmental concerns related to rice cultivation. Traits that help rice to maintain yield performance under minimum inputs like seedling vigor, appropriate root architecture for nutrient use efficiency should be incorporated into varieties for low input systems through integrated breeding approaches. Genes or QTLs controlling nutrient uptake, nutrient assimilation, nutrient remobilization, and root morphology need to be properly incorporated into the rice breeding pipeline. Also, genes/QTLs controlling suitable rice cultivars for sustainable farming. Since several variables influence performance under low input conditions, conventional breeding techniques make it challenging to work on many traits. However, recent advances in omics technologies have created enormous opportunities for rapidly improving multiple characteristics. This review highlights current research on features pertinent to low-input agriculture and provides an overview of alternative genomics-based breeding strategies for enhancing genetic gain in rice suitable for low-input farming practices.

To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.

PremiseThe agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping.MethodsWe developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images.ResultsThe rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28.DiscussionThese results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.