PlantCV v2: Image analysis software for high-throughput plant phenotyping

Deep Learning in Image-Based Plant Phenotyping

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

Twilight length alters growth and flowering time in Arabidopsis via LHY/CCA1

Decades of research have uncovered how plants respond to two environmental variables that change across latitudes and over seasons: photoperiod and temperature. However, a third such variable, twilight length, has so far gone unstudied. Here, using controlled growth setups, we show that the duration of twilight affects growth and flowering time via the LHY/CCA1 clock genes in the model plant Arabidopsis. Using a series of progressively truncated no-twilight photoperiods, we also found that plants are more sensitive to twilight length compared to equivalent changes in solely photoperiods. Transcriptome and proteome analyses showed that twilight length affects reactive oxygen species metabolism, photosynthesis, and carbon metabolism. Genetic analyses suggested a twilight sensing pathway from the photoreceptors PHY E, PHY B, PHY D, and CRY2 through LHY/CCA1 to flowering modulation through the GI-FT pathway. Overall, our findings call for more nuanced models of day-length perception in plants and posit that twilight is an important determinant of plant growth and development.

Integrity of xylan backbone affects plant responses to drought

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 xylanase-expressing 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.

Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.)

BACKGROUND

Lentil (Lens culinaris Medik.) is a globally-significant agricultural crop used to feed millions of people. Lentils have been cultivated in the Australian states of Victoria and South Australia for several decades, but efforts are now being made to expand their cultivation into Western Australia and New South Wales. Plant architecture plays a pivotal role in adaptation, leading to improved and stable yields especially in new expansion regions. Image-based high-throughput phenomics technologies provide opportunities for an improved understanding of plant development, architecture, and trait genetics. This paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform, which collected side-view RGB images at regular intervals under controlled photographic conditions throughout the experiment. A queue and distance-based algorithm that analysed morphological skeletons generated from images of lentil plants was developed in Python. This code was incorporated into an image analysis pipeline using open-source software (PlantCV) to measure the number, angle, and length of individual branches on lentil plants.

RESULTS

Branching structures could be accurately identified and quantified in immature plants, which is sufficient for calculating early vigour traits, however the accuracy declined as the plants matured. Absolute accuracy for branch counts was 77.9% for plants at 22 days after sowing (DAS), 57.9% at 29 DAS and 51.9% at 36 DAS. Allowing for an error of ± 1 branch, the associated accuracies for the same time periods were 97.6%, 90.8% and 79.2% respectively. Occlusion in more mature plants made the mapping of branches less accurate, but the information collected could still be useful for trait estimation. For branch length calculations, the amount of variance explained by linear mixed-effects models was 82% for geodesic length and 87% for Euclidean branch lengths. Within these models, both the mean geodesic and Euclidean distance measurements of branches were found to be significantly affected by genotype, DAS and their interaction. Two informative metrices were derived from the calculations of branch angle; 'splay' is a measure of how far a branch angle deviates from being fully upright whilst 'angle-difference' is the difference between the smallest and largest recorded branch angle on each plant. The amount of variance explained by linear mixed-effects models was 38% for splay and 50% for angle difference. These lower R2 values are likely due to the inherent difficulties in measuring these parameters, nevertheless both splay and angle difference were found to be significantly affected by cultivar, DAS and their interaction. When 276 diverse lentil genotypes with varying degrees of salt tolerance were grown in a glasshouse-based experiment where a portion were subjected to a salt treatment, the branching algorithm was able to distinguish between salt-treated and untreated lentil lines based on differences in branch counts. Likewise, the mean geodesic and Euclidean distance measurements of branches were both found to be significantly affected by cultivar, DAS and salt treatment. The amount of variance explained by the linear mixed-effects models was 57.8% for geodesic branch length and 46.5% for Euclidean branch length.

CONCLUSION

The methodology enabled the accurate quantification of the number, angle, and length of individual branches on glasshouse-grown lentil plants. This methodology could be applied to other dicotyledonous species.

Imprinting but not cytonuclear interactions determines seed size heterosis in Arabidopsis hybrids

The parent-of-origin effect on seeds can result from imprinting (unequal expression of paternal and maternal alleles) or combinational effects between cytoplasmic and nuclear genomes, but their relative contributions remain unknown. To discern these confounding factors, we produced cytoplasmic-nuclear substitution (CNS) lines using recurrent backcrossing in Arabidopsis (Arabidopsis thaliana) ecotypes Col-0 and C24. These CNS lines differed only in the nuclear genome (imprinting) or cytoplasm. The CNS reciprocal hybrids with the same cytoplasm displayed ∼20% seed size difference, whereas the seed size was similar between the reciprocal hybrids with fixed imprinting. Transcriptome analyses in the endosperm of CNS hybrids using laser-capture microdissection identified 104 maternally expressed genes (MEGs) and 90 paternally expressed genes (PEGs). These imprinted genes were involved in pectin catabolism and cell wall modification in the endosperm. Homeodomain Glabrous9 (HDG9), an epiallele and one of 11 cross-specific imprinted genes, affected seed size. In the embryo, there were a handful of imprinted genes in the CNS hybrids but only 1 was expressed at higher levels than in the endosperm. AT4G13495 was found to encode a long-noncoding RNA (lncRNA), but no obvious seed phenotype was observed in lncRNA knockout lines. Nuclear RNA Polymerase D1 (NRPD1), encoding the largest subunit of RNA Pol IV, was involved in the biogenesis of small interfering RNAs. Seed size and embryos were larger in the cross using nrpd1 as the maternal parent than in the reciprocal cross, supporting a role of the maternal NRPD1 allele in seed development. Although limited ecotypes were tested, these results suggest that imprinting and the maternal NRPD1-mediated small RNA pathway play roles in seed size heterosis in plant hybrids.

Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework

Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.

Premeiotic 24-nt phasiRNAs are present in the Zea genus and unique in biogenesis mechanism and molecular function

Reproductive phasiRNAs (phased, small interfering RNAs) are broadly present in angiosperms and play crucial roles in sustaining male fertility. While the premeiotic 21-nt (nucleotides) phasiRNAs and meiotic 24-nt phasiRNA pathways have been extensively studied in maize (Zea mays) and rice (Oryza sativa), a third putative category of reproductive phasiRNAs-named premeiotic 24-nt phasiRNAs-have recently been reported in barley (Hordeum vulgare) and wheat (Triticum aestivum). To determine whether premeiotic 24-nt phasiRNAs are also present in maize and related species and begin to characterize their biogenesis and function, we performed a comparative transcriptome and degradome analysis of premeiotic and meiotic anthers from five maize inbred lines and three teosinte species/subspecies. Our data indicate that a substantial subset of the 24-nt phasiRNA loci in maize and teosinte are already highly expressed at the premeiotic phase. The premeiotic 24-nt phasiRNAs are similar to meiotic 24-nt phasiRNAs in genomic origin and dependence on DCL5 (Dicer-like 5) for biogenesis, however, premeiotic 24-nt phasiRNAs are unique in that they are likely i) not triggered by microRNAs, ii) not loaded by AGO18 proteins, and iii) not capable of mediating PHAS precursor cleavage. In addition, we also observed a group of premeiotic 24-nt phasiRNAs in rice using previously published data. Together, our results indicate that the premeiotic 24-nt phasiRNAs constitute a unique class of reproductive phasiRNAs and are present more broadly in the grass family (Poaceae) than previously known.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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

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

The Combination of Low-Cost, Red-Green-Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

Multilevel analysis between Physcomitrium patens and Mortierellaceae endophytes explores potential long-standing interaction among land plants and fungi

The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.

Premeiotic 24-nt phasiRNAs are present in the Zea genus and unique in biogenesis mechanism and molecular function

Reproductive phasiRNAs are broadly present in angiosperms and play crucial roles in sustaining male fertility. While the premeiotic 21-nt phasiRNAs and meiotic 24-nt phasiRNA pathways have been extensively studied in maize (Zea mays) and rice (Oryza sativa), a third putative category of reproductive phasiRNAs-named premeiotic 24-nt phasiRNAs-have recently been reported in barley (Hordeum vulgare) and wheat (Triticum aestivum). To determine whether premeiotic 24-nt phasiRNAs are also present in maize and related species and begin to characterize their biogenesis and function, we performed a comparative transcriptome and degradome analysis of premeiotic and meiotic anthers from five maize inbred lines and three teosinte species/subspecies. Our data indicate that a substantial subset of the 24-nt phasiRNA loci in maize and teosinte are already highly expressed at premeiotic phase. The premeiotic 24-nt phasiRNAs are similar to meiotic 24-nt phasiRNAs in genomic origin and dependence on DCL5 for biogenesis, however, premeiotic 24-nt phasiRNAs are unique in that they are likely (i) not triggered by microRNAs, (ii) not loaded by AGO18 proteins, and (iii) not capable of mediating cis-cleavage. In addition, we also observed a group of premeiotic 24-nt phasiRNAs in rice using previously published data. Together, our results indicate that the premeiotic 24-nt phasiRNAs constitute a unique class of reproductive phasiRNAs and are present more broadly in the grass family (Poaceae) than previously known.

Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies

Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management.

Natural variation in Brachypodium distachyon responses to combined abiotic stresses

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.

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

Quantitative analysis of lateral root development with time-lapse imaging and deep neural network

During lateral root (LR) development, morphological alteration of the developing single LR primordium occurs continuously. Precise observation of this continuous alteration is important for understanding the mechanism involved in single LR development. Recently, we reported that very long-chain fatty acids are important signalling molecules that regulate LR development. In the study, we developed an efficient method to quantify the transition of single LR developmental stages using time-lapse imaging followed by a deep neural network (DNN) analysis. In this 'insight' paper, we discuss our DNN method and the importance of time-lapse imaging in studies on plant development. Integrating DNN analysis and imaging is a powerful technique for the quantification of the timing of the transition of organ morphology; it can become an important method to elucidate spatiotemporal molecular mechanisms in plant development.

Physcomitrium patens response to elevated CO2 is flexible and determined by an interaction between sugar and nitrogen availability

Mosses hold a unique position in plant evolution and are crucial for protecting natural, long-term carbon storage systems such as permafrost and bogs. Due to small stature, mosses grow close to the soil surface and are exposed to high levels of CO2 , produced by soil respiration. However, the impact of elevated CO2 (eCO2 ) levels on mosses remains underexplored. We determined the growth responses of the moss Physcomitrium patens to eCO2 in combination with different nitrogen levels and characterized the underlying physiological and metabolic changes. Three distinct growth characteristics, an early transition to caulonema, the development of longer, highly pigmented rhizoids, and increased biomass, define the phenotypic responses of P. patens to eCO2 . Elevated CO2 impacts growth by enhancing the level of a sugar signaling metabolite, T6P. The quantity and form of nitrogen source influences these metabolic and phenotypic changes. Under eCO2 , P. patens exhibits a diffused growth pattern in the presence of nitrate, but ammonium supplementation results in dense growth with tall gametophores, demonstrating high phenotypic plasticity under different environments. These results provide a framework for comparing the eCO2 responses of P. patens with other plant groups and provide crucial insights into moss growth that may benefit climate change models.

ShinyFruit: interactive fruit phenotyping software and its application in blackberry

Introduction

Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as genome-wide association studies and genomic selection.

Methods

We present an R-based web application called ShinyFruit for image-based phenotyping of size, shape, and color-related qualities in fruits and vegetables. Here, we have demonstrated one potential application for ShinyFruit by comparing its estimates of fruit length, width, and red drupelet reversion (RDR) with ImageJ and analogous manual phenotyping techniques in a population of blackberry cultivars and breeding selections from the University of Arkansas System Division of Agriculture Fruit Breeding Program.

Results

ShinyFruit results shared a strong positive correlation with manual measurements for blackberry length (r = 0.96) and ImageJ estimates of RDR (r = 0.96) and significant, albeit weaker, correlations with manual RDR estimation methods (r = 0.62 - 0.70). Neither phenotyping method detected genotypic differences in blackberry fruit width, suggesting that this trait is unlikely to be heritable in the population observed.

Discussion

It is likely that implementing a treatment to promote RDR expression in future studies might strengthen the documented correlation between phenotyping methods by maximizing genotypic variance. Even so, our analysis has suggested that ShinyFruit provides a viable, open-source solution to efficient phenotyping of size and color in blackberry fruit. The ability for users to adjust analysis settings should also extend its utility to a wide range of fruits and vegetables.

Quantifying physiological trait variation with automated hyperspectral imaging in rice

Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.

Imprinting but not cytonuclear interactions affects parent-of-origin effect on seed size in Arabidopsis hybrids

The parent-of-origin effect on seed size can result from imprinting or a combinational effect between cytoplasmic and nuclear genomes, but their relative contributions remain unknown. To discern these confounding effects, we generated cytoplasmic-nuclear substitution (CNS) lines using recurrent backcrossing in the Arabidopsis thaliana ecotypes Col-0 and C24. These CNS lines differ only in the nuclear genome (imprinting) or in the cytoplasm. The CNS reciprocal hybrids with the same cytoplasm display a ~20% seed size difference as observed in the conventional hybrids. However, seed size is similar between the reciprocal cybrids with fixed imprinting. Transcriptome analyses in the endosperm of CNS hybrids using laser-capture microdissection have identified 104 maternally expressed genes (MEGs) and 90 paternally-expressed genes (PEGs). These imprinted genes are involved in pectin catabolism and cell wall modification in the endosperm. HDG9, an epiallele and one of 11 cross-specific imprinted genes, controls seed size. In the embryo, a handful of imprinted genes is found in the CNS hybrids but only one is expressed higher in the embryo than endosperm. AT4G13495 encodes a long-noncoding RNA (lncRNA), but no obvious seed phenotype is observed in the lncRNA knockout lines. NRPD1, encoding the largest subunit of RNA Pol IV, is involved in the biogenesis of small interfering RNAs. Seed size and embryo is larger in the cross using nrpd1 as the maternal parent than in the reciprocal cross. In spite of limited ecotypes tested, these results suggest potential roles of imprinting and NRPD1-mediated small RNA pathway in seed size variation in hybrids.

A rapid method to quantify vein density in C4 plants using starch staining

C4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C4 plants compared with the ancestral C3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C4 species demonstrating natural variation for this trait.

Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging

A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.

RGB image-based method for phenotyping rust disease progress in pea leaves using R

BACKGROUND

Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.

RESULTS

A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error.

CONCLUSIONS

A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

Impact of xylan on field productivity and wood saccharification properties in aspen

Xylan that comprises roughly 25% of hardwood biomass is undesirable in biorefinery applications involving saccharification and fermentation. Efforts to reduce xylan levels have therefore been made in many species, usually resulting in improved saccharification. However, such modified plants have not yet been tested under field conditions. Here we evaluate the field performance of transgenic hybrid aspen lines with reduced xylan levels and assess their usefulness as short-rotation feedstocks for biorefineries. Three types of transgenic lines were tested in four-year field tests with RNAi constructs targeting either Populus GT43 clades B and C (GT43BC) corresponding to Arabidopsis clades IRX9 and IRX14, respectively, involved in xylan backbone biosynthesis, GATL1.1 corresponding to AtGALT1 involved in xylan reducing end sequence biosynthesis, or ASPR1 encoding an atypical aspartate protease. Their productivity, wood quality traits, and saccharification efficiency were analyzed. The only lines differing significantly from the wild type with respect to growth and biotic stress resistance were the ASPR1 lines, whose stems were roughly 10% shorter and narrower and leaves showed increased arthropod damage. GT43BC lines exhibited no growth advantage in the field despite their superior growth in greenhouse experiments. Wood from the ASPR1 and GT43BC lines had slightly reduced density due to thinner cell walls and, in the case of ASPR1, larger cell diameters. The xylan was less extractable by alkali but more hydrolysable by acid, had increased glucuronosylation, and its content was reduced in all three types of transgenic lines. The hemicellulose size distribution in the GALT1.1 and ASPR1 lines was skewed towards higher molecular mass compared to the wild type. These results provide experimental evidence that GATL1.1 functions in xylan biosynthesis and suggest that ASPR1 may regulate this process. In saccharification without pretreatment, lines of all three constructs provided 8-11% higher average glucose yields than wild-type plants. In saccharification with acid pretreatment, the GT43BC construct provided a 10% yield increase on average. The best transgenic lines of each construct are thus predicted to modestly outperform the wild type in terms of glucose yields per hectare. The field evaluation of transgenic xylan-reduced aspen represents an important step towards more productive feedstocks for biorefineries.

A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

Introduction

Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand plastic responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity.

Methods

Using a root imaging and analysis pipeline, the 4D architecture of the pennycress root system was characterized under four nitrate regimes, ranging from zero to high nitrate concentrations. These measurements were taken at four time points (days 5, 9, 13, and 17 after sowing).

Results

Significant nitrate condition response and genotype interactions were identified for many root traits, with the greatest impact observed on lateral root traits. In trace nitrate conditions, a greater lateral root count, length, density, and a steeper lateral root angle was observed compared to high nitrate conditions. Additionally, genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density.

Discussion

These findings illustrate root trait variance among pennycress accessions. These traits could serve as targets for breeding programs aimed at developing improved cover crops that are responsive to nitrate, leading to enhanced productivity, resilience, and ecosystem service.

The plant response to high CO2 levels is heritable and orchestrated by DNA methylation

Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO₂ ) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO₂ environment while transgenerational studies are rare. We aimed to determine transgenerational growth responses in plants after exposure to high CO₂ by investigating the direct progeny when returned to baseline CO₂ levels. We found that both the flowering plant Arabidopsis thaliana and seedless nonvascular plant Physcomitrium patens continue to display accelerated growth rates in the progeny of plants exposed to high CO₂ . We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response. More specifically, the pathway of RNA-directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO₂ exposure.

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

Drought stress detection technique for wheat crop using machine learning

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

Low-cost and automated phenotyping system "Phenomenon" for multi-sensor in situ monitoring in plant in vitro culture

BACKGROUND

The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.

RESULTS

An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.

CONCLUSION

The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

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.

Different combinations of laccase paralogs nonredundantly control the amount and composition of lignin in specific cell types and cell wall layers in Arabidopsis

Vascular plants reinforce the cell walls of the different xylem cell types with lignin phenolic polymers. Distinct lignin chemistries differ between each cell wall layer and each cell type to support their specific functions. Yet the mechanisms controlling the tight spatial localization of specific lignin chemistries remain unclear. Current hypotheses focus on control by monomer biosynthesis and/or export, while cell wall polymerization is viewed as random and nonlimiting. Here, we show that combinations of multiple individual laccases (LACs) are nonredundantly and specifically required to set the lignin chemistry in different cell types and their distinct cell wall layers. We dissected the roles of Arabidopsis thaliana LAC4, 5, 10, 12, and 17 by generating quadruple and quintuple loss-of-function mutants. Loss of these LACs in different combinations led to specific changes in lignin chemistry affecting both residue ring structures and/or aliphatic tails in specific cell types and cell wall layers. Moreover, we showed that LAC-mediated lignification has distinct functions in specific cell types, waterproofing fibers, and strengthening vessels. Altogether, we propose that the spatial control of lignin chemistry depends on different combinations of LACs with nonredundant activities immobilized in specific cell types and cell wall layers.

Different combinations of laccase paralogs nonredundantly control the amount and composition of lignin in specific cell types and cell wall layers in Arabidopsis

Vascular plants reinforce the cell walls of the different xylem cell types with lignin phenolic polymers. Distinct lignin chemistries differ between each cell wall layer and each cell type to support their specific functions. Yet the mechanisms controlling the tight spatial localization of specific lignin chemistries remain unclear. Current hypotheses focus on control by monomer biosynthesis and/or export, while cell wall polymerization is viewed as random and nonlimiting. Here, we show that combinations of multiple individual laccases (LACs) are nonredundantly and specifically required to set the lignin chemistry in different cell types and their distinct cell wall layers. We dissected the roles of Arabidopsis thaliana LAC4, 5, 10, 12, and 17 by generating quadruple and quintuple loss-of-function mutants. Loss of these LACs in different combinations led to specific changes in lignin chemistry affecting both residue ring structures and/or aliphatic tails in specific cell types and cell wall layers. Moreover, we showed that LAC-mediated lignification has distinct functions in specific cell types, waterproofing fibers, and strengthening vessels. Altogether, we propose that the spatial control of lignin chemistry depends on different combinations of LACs with nonredundant activities immobilized in specific cell types and cell wall layers.

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

Automating the analysis of plants using image processing would help remove barriers to phenotyping and large-scale precision agricultural technologies, such as site-specific weed control. The combination of accessible hardware and high-performance deep learning (DL) tools for plant analysis is becoming widely recognised as a path forward for both plant science and applied precision agricultural purposes. Yet, a lack of collaboration in image analysis for plant science, despite the open-source origins of much of the technology, is hindering development. Here, we show how tools developed for specific attributes of phenotyping or weed recognition for precision weed control have substantial overlapping data structure, software/hardware requirements and outputs. An open-source approach to these tools facilitates interdisciplinary collaboration, avoiding unnecessary repetition and allowing research groups in both basic and applied sciences to capitalise on advancements and resolve respective bottlenecks. The approach mimics that of machine learning in its nascence. Three areas of collaboration are identified as critical for improving efficiency, (1) standardized, open-source, annotated dataset development with consistent metadata reporting; (2) establishment of accessible and reliable training and testing platforms for DL algorithms; and (3) sharing of all source code used in the research process. The complexity of imaging plants and cost of annotating image datasets means that collaboration from typically distinct fields will be necessary to capitalize on the benefits of DL for both applied and basic science purposes.

Analyzing angiogenesis on a chip using deep learning-based image processing

Angiogenesis, the formation of new blood vessels from existing vessels, has been associated with more than 70 diseases. Although numerous studies have established angiogenesis models, only a few indicators can be used to analyze angiogenic structures. In the present study, we developed an image-processing pipeline based on deep learning to analyze and quantify angiogenesis. We utilized several image-processing algorithms to quantify angiogenesis, including a deep learning-based cell nuclear segmentation algorithm and image skeletonization. This method could quantify and measure changes in blood vessels in response to biochemical gradients using 16 indicators, including length, width, number, and nuclear distribution. Moreover, this procedure is highly efficient for the three-dimensional quantitative analysis of angiogenesis and can be applied to diverse angiogenesis investigations.

AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone

Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient's condition.

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

PREMISE

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

METHODS

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

RESULTS

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

DISCUSSION

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

Effects of Poty-Potexvirus Synergism on Growth, Photosynthesis and Metabolite Status of Nicotiana benthamiana

Mixed virus infections threaten crop production because interactions between the host and the pathogen mix may lead to viral synergism. While individual infections by potato virus A (PVA), a potyvirus, and potato virus X (PVX), a potexvirus, can be mild, co-infection leads to synergistic enhancement of PVX and severe symptoms. We combined image-based phenotyping with metabolite analysis of single and mixed PVA and PVX infections and compared their effects on growth, photosynthesis, and metabolites in Nicotiana benthamiana. Viral synergism was evident in symptom severity and impaired growth in the plants. Indicative of stress, the co-infection increased leaf temperature and decreased photosynthetic parameters. In contrast, singly infected plants sustained photosynthetic activity. The host's metabolic response differed significantly between single and mixed infections. Over 200 metabolites were differentially regulated in the mixed infection: especially defense-related metabolites and aromatic and branched-chain amino acids increased compared to the control. Changes in the levels of methionine cycle intermediates and a low S-adenosylmethionine/S-adenosylhomocysteine ratio suggested a decline in the methylation potential in co-infected plants. The decreased ratio between reduced glutathione, an important scavenger of reactive oxygen species, and its oxidized form, indicated that severe oxidative stress developed during co-infection. Based on the results, infection-associated oxidative stress is successfully controlled in the single infections but not in the synergistic infection, where activated defense pathways are not sufficient to counter the impact of the infections on plant growth.

Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana

Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate the engineering of plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1,135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focusing on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with numerous genes, most of which were not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits, while most are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population, we were able to identify unique nitrogen responses, such as preferring ammonium or nitrate, which appear to be generated by combinations of loci rather than a few large-effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large-effect single gene manipulations.

Novel method for the quantification of rosette area from images of Arabidopsis seedlings grown on agar plates

PREMISE

The 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.

METHODS

We 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.

RESULTS

The 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.

DISCUSSION

These 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.

A low-cost high-throughput phenotyping system for automatically quantifying foliar area and greenness

PREMISE

With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness.

METHODS

During a greenhouse experiment on the effects of abiotic stress on Brassica rapa, we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000.

RESULTS

In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not.

DISCUSSION

These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.

Stb6 mediates stomatal immunity, photosynthetic functionality, and the antioxidant system during the Zymoseptoria tritici-wheat interaction

This study offers new perspectives on the biochemical and physiological changes that occur in wheat following a gene-for-gene interaction with the fungal pathogen Zymoseptoria tritici. The Z. tritici isolate IPO323, carries AvrStb6, while ΔAvrStb6#33, lacks AvrStb6. The wheat cultivar (cv.) Shafir, bears the corresponding resistance gene Stb6. Inoculation of cv. Shafir with these isolates results in two contrasted phenotypes, offering a unique opportunity to study the immune response caused by the recognition of AvrStb6 by Stb6. We employed a variety of methodologies to dissect the physiological and biochemical events altered in cv. Shafir, as a result of the AvrStb6-Stb6 interaction. Comparative analysis of stomatal conductance demonstrated that AvrStb6-Stb6 mediates transient stomatal closures to restrict the penetration of Zymoseptoria tritici. Tracking photosynthetic functionality through chlorophyll fluorescence imaging analysis demonstrated that AvrStb6-Stb6 retains the functionality of photosynthesis apparatus by promoting Non-Photochemical Quenching (NPQ). Furthermore, the PlantCV image analysis tool was used to compare the H₂O₂ accumulation and incidence of cell death (2, 4, 8, 12, 16, and 21 dpi), over Z. tritici infection. Finally, our research shows that the AvrStb6-Stb6 interaction coordinates the expression and activity of antioxidant enzymes, both enzymatic and non-enzymatic, to counteract oxidative stress. In conclusion, the Stb6-AvrStb6 interaction in the Z. tritici-wheat pathosystem triggers transient stomatal closure and maintains photosynthesis while regulating oxidative stress.

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

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

JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis

Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U²-Net; and (4) leaf segmentation with U²-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.

Growth-limiting drought stress induces time-of-day-dependent transcriptome and physiological responses in hybrid poplar

Drought stress negatively impacts the health of long-lived trees. Understanding the genetic mechanisms that underpin response to drought stress is requisite for selecting or enhancing climate change resilience. We aimed to determine how hybrid poplars respond to prolonged and uniform exposure to drought; how responses to moderate and more severe growth-limiting drought stresses differed; and how drought responses change throughout the day. We established hybrid poplar trees (Populus × 'Okanese') from unrooted stem cutting with abundant soil moisture for 6 weeks. We then withheld water to establish well-watered, moderate and severe growth-limiting drought conditions. These conditions were maintained for 3 weeks during which growth was monitored. We then measured photosynthetic rates and transcriptomes of leaves that had developed during the drought treatments at two times of day. The moderate and severe drought treatments elicited distinct changes in growth and development, photosynthetic rates and global transcriptome profiles. Notably, the time of day of sampling produced the strongest effect in the transcriptome data. The moderate drought treatment elicited global transcriptome changes that were intermediate to the severe and well-watered treatments in the early evening but did not elicit a strong drought response in the morning. Stable drought conditions that are sufficient to limit plant growth elicit distinct transcriptional profiles depending on the degree of water limitation and on the time of day at which they are measured. There appears to be a limited number of genes and functional gene categories that are responsive to all of the tested drought conditions in this study emphasizing the complex nature of drought regulation in long-lived trees.

Abscisic acid-controlled redox proteome of Arabidopsis and its regulation by heterotrimeric Gβ protein

The plant hormone abscisic acid (ABA) plays crucial roles in regulation of stress responses and growth modulation. Heterotrimeric G-proteins are key mediators of ABA responses. Both ABA and G-proteins have also been implicated in intracellular redox regulation; however, the extent to which reversible protein oxidation manipulates ABA and/or G-protein signaling remains uncharacterized. To probe the role of reversible protein oxidation in plant stress response and its dependence on G-proteins, we determined the ABA-dependent reversible redoxome of wild-type and Gβ-protein null mutant agb1 of Arabidopsis. We quantified 6891 uniquely oxidized cysteine-containing peptides, 923 of which show significant changes in oxidation following ABA treatment. The majority of these changes required the presence of G-proteins. Divergent pathways including primary metabolism, reactive oxygen species response, translation and photosynthesis exhibited both ABA- and G-protein-dependent redox changes, many of which occurred on proteins not previously linked to them. We report the most comprehensive ABA-dependent plant redoxome and uncover a complex network of reversible oxidations that allow ABA and G-proteins to rapidly adjust cellular signaling to adapt to changing environments. Physiological validation of a subset of these observations suggests that functional G-proteins are required to maintain intracellular redox homeostasis and fully execute plant stress responses.

Multi-omic analysis shows REVEILLE clock genes are involved in carbohydrate metabolism and proteasome function

Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE (RVE) proteins are recently discovered members of the plant circadian circuitry that activate the evening complex and PSEUDO-RESPONSE REGULATOR genes to maintain regular circadian oscillation. The RVE8 protein and its two homologs, RVE 4 and 6 in Arabidopsis (Arabidopsis thaliana), have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size, and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw new connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid, and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset provides substantial insight into the far-reaching impact RVE8-like proteins have on the diel plant cell environment.