Use of Blue-Green Fluorescence and Thermal Imaging in the Early Detection of Sunflower Infection by the Root Parasitic Weed Orobanche cumana Wallr

Broomrape-host interaction: host morphology and physiology as metrics for infestation

MAIN CONCLUSION

In contrast to other plant pests, broomrape, parasitic plant, rely on maintaining the productivity of the host plant to complete their life cycle. Parasitic plants, particularly those in the Orobanchaceae family, rely on their host plants to complete their life cycle. Unlike other plant parasites such as fungi and bacteria, which exploit their hosts regardless of their physiological status, parasitic plants development is linked to the host productivity due to their mutual physiological dependence on water availability and sugar metabolism. Presently, most research focuses on the damage caused to the host after the parasite completes its life cycle, including inflorescence emergence and seed dispersal. However, the interaction between parasite and host begins long before these stages. This implies that certain physiological adaptations are necessary to sustain the parasite's development while maintaining the host's productivity. In this review, I compile existing knowledge regarding changes in host physiology during the early developmental stages of parasitic plants, spanning from attachment to inflorescence emergence. Additionally, I highlight knowledge gaps that should be addressed to understand how hosts sustain themselves throughout extended periods of parasitism.

Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameters on host crop Helianthus annuus

KEY MESSAGE

This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

Effect of Epichloë Endophyte on the Growth and Carbon Allocation of Its Host Plant Stipa purpurea under Hemiparasitic Root Stress

Epichloë endophytes not only affect the growth and resistance of their host plants but also confer nutrient benefits to parasitized hosts. In this study, we used Pedicularis kansuensis to parasitize Stipa purpurea, both with and without endophytic fungi, and to establish a parasitic system. In this study, endophytic fungal infection was found to increase the dry weight of the leaf, stem, and leaf sheath, as well as the plant height, root length, tiller number, aboveground biomass, and underground biomass of S. purpurea under root hemiparasitic stress. Meanwhile, the 13C allocation of the leaf sheaths and roots of S. purpurea increased as the density of P. kansuensis increased, while the 13C allocation of the leaf sheaths and roots of E+ S. purpurea was lower than that of E- S. purpurea. The 13C allocation of the stem, leaf sheath, and root of E+ S. purpurea was higher than that of its E- counterpart. Furthermore, the content of photosynthetic 13C and the 13C partition rate of the stems, leaves, roots, and entire plant of S. purpurea and P. kansuensis transferred from S. purpurea increased as the density of P. kansuensis increased. These results will generate new insights into the potential role of symbiotic microorganisms in regulating the interaction between root hemiparasites and their hosts.

Hydrogel-extraction technique for non-invasive detection of blue fluorescent substances in plant leaves

This paper reports a new hydrogel extraction technique for detecting blue fluorescent substances in plant leaves. These blue fluorescent substances were extracted by placing a hydrogel film on the leaf of a cherry tomato plant infected with Ralstonia solanacearum; herein, chlorogenic acid was confirmed to be a blue fluorescent substance. The wavelength at the maximum fluorescence intensity of the film after the hydrogel extraction was similar to that of the methanolic extract obtained from the infected cherry tomato leaves. Chlorophyll was not extracted from the hydrogel film because no fluorescence peak was observed at 680 nm. Accordingly, the blue fluorescence of the substances extracted from the hydrogel film was not quenched by the strong absorption of chlorophyll in the blue light region. This hydrogel extraction technique can potentially detect small amounts of blue fluorescent substances and the changes in its amount within the leaves of infected plants. These changes in the amount of blue fluorescent substances in the early stages of infection can be used to detect presymptomatic infections. Therefore, hydrogel extraction is a promising technique for the noninvasive detection of infections before onset.

Pre-Symptomatic Detection of Viral Infection in Tobacco Leaves Using PAM Fluorometry

Chlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana. Infection-induced changes in chlorophyll fluorescence parameters (quantum yield of photosystem II photochemistry (ΦPSII) and non-photochemical fluorescence quenching (NPQ)) in the non-inoculated leaf were recorded and compared with the spatial distribution of the virus detected by the fluorescence of GFP associated with the virus. We determined infection-related changes at different points of the light-induced chlorophyll fluorescence kinetics and at different days after inoculation. A slight change in the light-adapted steady-state values of ΦPSII and NPQ was observed in the infected area of the non-inoculated leaf. In contrast to the steady-state parameters, the dynamics of ΦPSII and NPQ caused by the dark-light transition in healthy and infected areas differed significantly starting from the second day after the detection of the virus in a non-inoculated leaf. The coefficients of correlation between chlorophyll fluorescence parameters and virus localization were 0.67 for ΦPSII and 0.76 for NPQ. In general, the results demonstrate the possibility of reliable pre-symptomatic detection of the spread of a viral infection using chlorophyll fluorescence imaging.

Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots

BACKGROUND

The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.

RESULTS

We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.

CONCLUSION

This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Circular drought-hardening confers drought tolerance via modulation of the antioxidant defense system, osmoregulation, and gene expression in tobacco

Drought stress hinders the growth and development of crop plants and ultimately its productivity. It is expected that drought stress will be frequent and intense in the future due to drastic changes in the global climate. It is necessary to make crop plants more resilient to drought stress through various techniques; drought-hardening is one of them. Defining various metabolic strategies used by tobacco plants to confer drought tolerance will be important for maintaining plant physiological functions, but studies addressing this topic are limited. This study was designed to elucidate the drought tolerance and adaptation strategies used by tobacco plants via the application of different circular drought-hardening cycles (control: no drought-hardening, T1: one cycle of drought hardening, T2: two cycles of drought-hardening, and T3: three cycles of drought-hardening) to two tobacco varieties namely Honghuadajinyuan (H) and Yun Yan-100 (Y). The results revealed that drought-hardening decreased the fresh and dry biomass of the tobacco plants. The decrease was more pronounced in the T3 treatment for both H (23 and 29%, respectively) and Y (26 and 31%, respectively) under drought stress. The MDA contents, especially in T1 and T2 in both varieties, were statistically similar compared with control under drought stress. Similarly, higher POD, APX, and GR activities were observed, especially in T3, and elevated amounts of AsA and GSH were also observed among the different circular drought-hardening treatments under drought stress. Thus circular drought-hardening mitigated the oxidative damage by increasing the antioxidant enzyme activities and elevated the content of antioxidant substances, a key metabolic strategy under drought stress. Similarly, another important plant metabolic strategy is the osmotic adjustment. Different circular drought-hardening treatments improved the accumulation of proline and soluble sugars contents which contributed to osmoregulation. Finally, at the molecular level, circular drought-hardening improved the transcript levels of antioxidant enzyme-related genes (CAT, APX1, and GR2), proline and polyamines biosynthesis-related genes (P5CS1 and ADC2), and ABA signaling (SnRK2), and transcription factors (AREB1 and WRKY6) in response to drought stress. As a result, circular drought-hardening (T2 and T3 treatments) promoted tolerance to water stress via affecting the anti-oxidative capacity, osmotic adjustment, and regulation of gene expression in tobacco.

Thermal Imaging for Plant Stress Detection and Phenotyping

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation

As an esthetic trait, ray floret color has a high importance in the development of new sunflower genotypes and their market value. Standard methodology for the evaluation of sunflower ray florets is based on International Union for the Protection of New Varieties of Plants (UPOV) guidelines for sunflower. The major deficiency of this methodology is the necessity of high expertise from evaluators and its high subjectivity. To test the hypothesis that humans cannot distinguish colors equally, six commercial sunflower genotypes were evaluated by 100 agriculture experts, using UPOV guidelines. Moreover, the paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis but still leaves the final decision to the evaluator. For this purpose, we created a new Flower Color Image Analysis (FloCIA) software for sunflower ray floret digital image segmentation and automatic classification into one of the categories given by the UPOV guidelines. To assess the benefits and relevance of this method, accuracy of the newly developed software was studied by comparing 153 digital photographs of F₂ genotypes with expert evaluator answers which were used as the ground truth. The FloCIA enabled visualizations of segmentation of ray floret images of sunflower genotypes used in the study, as well as two dominant color clusters, percentages of pixels belonging to each UPOV color category with graphical representation in the CIE (International Commission on Illumination) L^∗a^∗b^∗ (or simply Lab) color space in relation to the mean vectors of the UPOV category. Precision (repeatability) of ray flower color determination was greater between dUPOV based expert color evaluation and software evaluation than between two UPOV based evaluations performed by the same expert. The accuracy of FloCIA software used for unsupervised (automatic) classification was 91.50% on the image dataset containing 153 photographs of F₂ genotypes. In this case, the software and the experts had classified 140 out of 153 of images in the same color categories. This visual presentation can serve as a guideline for evaluators to determine the dominant color and to conclude if more than one significant color exists in the examined genotype.

Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.

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.

Genetic and Genomic Tools in Sunflower Breeding for Broomrape Resistance

Broomrape is a root parasitic plant causing yield losses in sunflower production. Since sunflower is an important oil crop, the development of broomrape-resistant hybrids is the prime breeding objective. Using conventional plant breeding methods, breeders have identified resistant genes and developed a number of hybrids resistant to broomrape, adapted to different growing regions worldwide. However, the spread of broomrape into new countries and the development of new and more virulent races have been noted intensively. Recent advances in sunflower genomics provide additional tools for plant breeders to improve resistance and find durable solutions for broomrape spread and virulence. This review describes the structure and distribution of new, virulent physiological broomrape races, sources of resistance for introduction into susceptible cultivated sunflower, qualitative and quantitative resistance genes along with gene pyramiding and marker assisted selection (MAS) strategies applied in the process of increasing sunflower resistance. In addition, it presents an overview of underutilized biotechnological tools, such as phenotyping, -omics, and genome editing techniques, which need to be introduced in the study of sunflower resistance to broomrape in order to achieve durable resistance.

Opportunities and Limitations of Crop Phenotyping in Southern European Countries

The Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of "phytotron research" to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainability.

The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes

Lentil (Lens culinaris, Medik.) is an important legume crop, which often experience drought stress especially at the flowering and grain filling phenological stages. The availability of efficient and robust screening tools based on relevant non-destructive quantifiable traits would facilitate research on crop improvement for drought tolerance. The objective of this study was to evaluate the drought tolerance of 37 lentil genotypes using infrared thermal imaging (IRTI), drought tolerance parameters and multivariate data analysis. Potted plants were kept in a completely randomized design in a growth chamber with five replicates. Plants were subjected to three different drought treatments: 100, 50 and 20% of field capacity at the onset of reproductive period. The relative drought stress tolerance was determined based on a set of morpho-physiological parameters including non-destructive measures based on IRTI, such as: canopy temperature (Tc), canopy temperature depression (CTD) and crop water stress index (CWSI) during the growing period and destructive measures at harvest, such as: dry root-shoot ratio (RS ratio), relative water content (RWC) and harvest index (HI). The drought tolerance indices used were drought susceptibility index (DSI) and drought tolerance efficiency (DTE). Results showed that drought stress treatments significantly reduced the RWC, HI, CTD and DSI, whereas, the values of Tc, CWSI, RS ratio and DTE significantly increased for all the genotypes. The cluster analysis from morpho-physiological parameters clustered genotypes in three distinctive groups as per the level of drought stress tolerance. The genotypes with higher values of RS ratio, RWC, HI, DTE and CTD and lower values of DSI, Tc and CWSI were identified as drought-tolerant genotypes. Based on this preliminary screening, the genotypes Digger, Cumra, Indianhead, ILL 5588, ILL 6002 and ILL 5582 were identified as promising drought-tolerant genotypes. It can be concluded that the IRTI analysis is a high-throughput constructive screening tool along with RS ratio, RWC, HI and other drought tolerance indices to define the drought stress tolerance variability within lentil plants. These results provide a foundation for future research directed at identifying powerful drought assessment traits using rapid and non-destructive techniques, such as IRTI along with the yield traits, and understanding the biochemical and molecular mechanisms underlying lentil tolerance to drought stress.