Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Improving Common Bacterial Blight Phenotyping by Using Rub Inoculation and Machine Learning: Cheaper, Better, Faster, Stronger

Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant-pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and X. citri pv. fuscans is a major threat to common bean. The pathogenicity of these bacteria is variable among strains and depends mainly on a type III secretion system and associated type III effectors such as transcription activator-like effectors. Because the impact of a single gene is often small and difficult to detect, a discriminating methodology is required to distinguish the slight phenotype changes induced during the progression of the disease. Here, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains. Interestingly, rub inoculation of the first leaves combined with symptom assessment by machine learning-based imaging allowed significant distinction between wild-type and mutant strains. By contrast, dip inoculation of first-trifoliate leaves combined with chlorophyll fluorescence imaging did not differentiate the strains. Furthermore, the new method developed here led to the miniaturization of pathogenicity tests and significant time savings.

Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.

Chlorophyll content and fluorescence as physiological parameters for monitoring Orobanche foetida Poir. infection in faba bean

Orobanche spp. are root parasitic plants that cause yield losses in faba bean (Vicia faba L.). In Tunisia, O. crenata and O. foetida are among the major problems limiting faba bean production and productivity. Breeding for resistance and development of resistant varieties remain the most efficient control strategy to combat these parasites. In our study, field trials were conducted over two consecutive cropping seasons. A set of 42 genotypes were used in this study; 39 advanced lines and three checks; Najeh and Baraca (resistant) and Badi (susceptible). The trials were conducted in highly infested O. foetida plot at Oued-Beja Research Station in Tunisia. Results showed that advanced lines XAR-VF00.13-1-2-1-2-1 and XBJ90.04-2-3-1-1-1-2A expressed high resistance level exceeding those recorded for resistance checks Najeh and Baraca. O. foetida significantly affected the biomass, grain yield, chlorophyll content index (CCI) and the maximum quantum efficiency (Fv/Fm ratio). No significant effect was observed on host plant water content (WC). CCI decreases varied from 46.4% for the susceptible check Badi and 4.2% and 9.3%, respectively, for Baraca and XBJ90.04-2-3-1-1-1-2A. Orobanche parasitism resulted in a slight decreases of Fv/Fm ratio for the advanced lines XBJ90.04-2-3-1-1-1-2A and XAR-VF00.13-1-2-1-2-1 against important decreases observed for Badi and Baraca. Correlation between resistance to O. foetida and CCI and Fv/Fm makes this, easy-to-measure, parameter very useful as a practical screening tool for early parasitism detection, diagnosis and identification and selection of high resistant plants against this parasite.

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

Remote Sensing of Diseases

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.

Common bean resistance to Xanthomonas is associated with upregulation of the salicylic acid pathway and downregulation of photosynthesis

BACKGROUND

Common bacterial blight (CBB) caused by Xanthomonas phaseoli pv. phaseoli and Xanthomonas citri pv. fuscans is one of the major threats to common bean crops (Phaseolus vulgaris L.). Resistance to CBB is particularly complex as 26 quantitative resistance loci to CBB have been described so far. To date, transcriptomic studies after CBB infection have been very scarce and the molecular mechanisms underlying susceptibility or resistance are largely unknown.

RESULTS

We sequenced and annotated the genomes of two common bean genotypes being either resistant (BAT93) or susceptible (JaloEEP558) to CBB. Reciprocal BLASTp analysis led to a list of 20,787 homologs between these genotypes and the common bean reference genome (G19833), which provides a solid dataset for further comparative analyses. RNA-Seq after inoculation with X. phaseoli pv. phaseoli showed that the susceptible genotype initiated a more intense and diverse biological response than the resistant genotype. Resistance was linked to upregulation of the salicylic acid pathway and downregulation of photosynthesis and sugar metabolism, while susceptibility was linked to downregulation of resistance genes and upregulation of the ethylene pathway and of genes involved in cell wall modification.

CONCLUSIONS

This study helps better understanding the mechanisms occurring during the early colonization phase of common bean by Xanthomonas and unveils new actors potentially important for resistance and susceptibility to CBB. We discuss the potential link between the pathways induced during bean colonization and genes induced by transcription activator-like effectors (TALEs), as illustrated in other Xanthomonas pathovars.

Common bean resistance to Xanthomonas is associated with upregulation of the salicylic acid pathway and downregulation of photosynthesis

Background

Common bacterial blight (CBB) caused by Xanthomonas phaseoli pv. phaseoli and Xanthomonas citri pv. fuscans is one of the major threats to common bean crops (Phaseolus vulgaris L.). Resistance to CBB is particularly complex as 26 quantitative resistance loci to CBB have been described so far. To date, transcriptomic studies after CBB infection have been very scarce and the molecular mechanisms underlying susceptibility or resistance are largely unknown.

Results

We sequenced and annotated the genomes of two common bean genotypes being either resistant (BAT93) or susceptible (JaloEEP558) to CBB. Reciprocal BLASTp analysis led to a list of 20,787 homologs between these genotypes and the common bean reference genome (G19833), which provides a solid dataset for further comparative analyses. RNA-Seq after inoculation with X. phaseoli pv. phaseoli showed that the susceptible genotype initiated a more intense and diverse biological response than the resistant genotype. Resistance was linked to upregulation of the salicylic acid pathway and downregulation of photosynthesis and sugar metabolism, while susceptibility was linked to downregulation of resistance genes and upregulation of the ethylene pathway and of genes involved in cell wall modification.

Conclusions

This study helps better understanding the mechanisms occurring during the early colonization phase of common bean by Xanthomonas and unveils new actors potentially important for resistance and susceptibility to CBB. We discuss the potential link between the pathways induced during bean colonization and genes induced by transcription activator-like effectors (TALEs), as illustrated in other Xanthomonas pathovars.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.

High-throughput phenotyping for crop improvement in the genomics era

Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.

Phenomic Approaches and Tools for Phytopathologists

Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.

Involvement of Potassium Transport Systems in the Response of Synechocystis PCC 6803 Cyanobacteria to External pH Change, High-Intensity Light Stress and Heavy Metal Stress

The unicellular photosynthetic cyanobacterium, able to survive in varying environments, is the only prokaryote that directly converts solar energy and CO2 into organic material and is thus relevant for primary production in many ecosystems. To maintain the intracellular and intrathylakoid ion homeostasis upon different environmental challenges, the concentration of potassium as a major intracellular cation has to be optimized by various K(+)uptake-mediated transport systems. We reveal here the specific and concerted physiological function of three K(+)transporters of the plasma and thylakoid membranes, namely of SynK (K(+)channel), KtrB (Ktr/Trk/HKT) and KdpA (Kdp) in Synechocystis sp. strain PCC 6803, under specific stress conditions. The behavior of the wild type, single, double and triple mutants was compared, revealing that only Synk contributes to heavy metal-induced stress, while only Ktr/Kdp is involved in osmotic and salt stress adaptation. With regards to pH shifts in the external medium, the Kdp/Ktr uptake systems play an important role in the adaptation to acidic pH. Ktr, by affecting the CO2 concentration mechanism via its action on the bicarbonate transporter SbtA, might also be responsible for the observed effects concerning high-light stress and calcification. In the case of illumination with high-intensity light, a synergistic action of Kdr/Ktp and SynK is required in order to avoid oxidative stress and ensure cell viability. In summary, this study dissects, using growth tests, measurement of photosynthetic activity and analysis of ultrastructure, the physiological role of three K(+)transporters in adaptation of the cyanobacteria to various environmental changes.

Fluorescence Imaging in the Red and Far-Red Region during Growth of Sunflower Plantlets. Diagnosis of the Early Infection by the Parasite Orobanche cumana

Broomrape, caused by the root holoparasite Orobanche cumana, is the main biotic constraint to sunflower oil production worldwide. By the time broomrape emerges, most of the metabolic imbalance has been produced by O. cumana to sunflower plants. UV-induced multicolor fluorescence imaging (MCFI) provides information on the fluorescence emitted by chlorophyll (Chl) a of plants in the spectral bands with peaks near 680 nm (red, F680) and 740 nm (far-red, F740). In this work MCFI was extensively applied to sunflowers, either healthy or parasitized plants, for the first time. The distribution of red and far-red fluorescence was analyzed in healthy sunflower grown in pots under greenhouse conditions. Fluorescence patterns were analyzed across the leaf surface and throughout the plant by comparing the first four leaf pairs (LPs) between the second and fifth week of growth. Similar fluorescence patterns, with a delay of 3 or 4 days between them, were obtained for LPs of healthy sunflower, showing that red and far-red fluorescence varied with the developmental stage of the leaf. The use of F680 and F740 as indicators of sunflower infection by O. cumana during underground development stages of the parasite was also evaluated under similar experimental conditions. Early increases in F680 and F740 as well as decreases in F680/F740 were detected upon infection by O. cumana. Significant differences between inoculated and control plants depended on the LP that was considered at any time. Measurements of Chl contents and final total Chl content supported the results of MCFI, but they were less sensitive in differentiating healthy from inoculated plants. Sunflower infection was confirmed by the presence of broomrape nodules in the roots at the end of the experiment. The potential of MCFI in the red and far-red region for an early detection of O. cumana infection in sunflower was revealed. This technique might have a particular interest for early phenotyping in sunflower breeding programs. To our knowledge, this is the first work where the effect of a parasitic plant in its host is analyzed by means of fluorescence imaging in the red and far-red spectral regions.