Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

Hormonal involvement in boosting the vegetative vigour underlying caffeine-related improvements in lentil production

Previous studies have shown that caffeine (1,3,7-trimethylxanthine) has some potential for its use as a biostimulant ingredient for boosting lentil production at suboptimal temperatures. However, some limitations to its use include its potential side effects as an emerging contaminant and the current lack of knowledge of its mechanism of action. Here, we aimed to study the mechanisms underlying improved lentil production upon caffeine application. Greenhouse-grown plants treated with caffeine (at 10-5 M, 10-4 M, and 10-3 M) were compared to an untreated, control treatment, and both reproductive and vegetative vigour were evaluated in parallel with endogenous foliar concentrations of phytohormones, including both stress and growth-related hormones. Results showed an enhanced lentil production at the highest caffeine concentration (10-3 M) which might be attributed, at least in part, to a greater vegetative vigour. The hormonal profiling revealed a dual effect. Firstly, there was a specific increase in jasmonoyl-isoleucine (JA-Ile) in the short term, which may provide a priming effect. Secondly, abscisic acid (ABA) content kept at low levels and the active cytokinin (CK) isopentenyl adenine (2-iP) increased and persisted at high levels throughout the reproductive stage. Cytokinin-mediated effects on growth, and more specifically the high CK/ABA ratios in leaves, appeared to mediate caffeine-related effects in boosting vegetative vigour. In conclusion, caffeine emerges as a compelling alkaloid for integration into biostimulant formulations due to its favorable effect in boosting lentil production through an improvement of vegetative vigour. These outcomes appear to be modulated by phytohormones, most notably jasmonates, priming plants for improved performance under suboptimal temperatures, and cytokinins, alongside ABA and its associated ratios, collectively enhancing plant growth and reproductive vigour in challenging conditions.

Image-based plant wilting estimation

BACKGROUND

Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress.

RESULTS

We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress.

CONCLUSION

Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.

Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping

Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.

Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea L.) germplasm

Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Physiological and Structural Responses of Olive Leaves Related to Tolerance/Susceptibility to Verticillium dahliae

Verticillium wilt of olive (VWO), caused by the soil borne fungus Verticillium dahliae, is one of the most relevant diseases affecting this crop worldwide. One of the best VWO management strategies is the use of tolerant cultivars. Scarce information is available about physiological and structural responses in the leaves of olive cultivars displaying different levels of tolerance to VWO. To identify links between this phenotype and variations in functional characteristics of the leaves, this study examined the structural and physiological traits and the correlations among them in different olive varieties. This evaluation was conducted in the presence/absence of V. dahliae. On the one hand, no leaf trait but the area was related to VWO tolerance in the absence of the pathogen. On the other hand, after inoculation, susceptible cultivars showed lower leaf area and higher leaf mass per area and dry matter content. Furthermore, at the physiological level, these plants showed severe symptoms resembling water stress. Analyzing the relationships among physiological and structural traits revealed differences between tolerant and susceptible cultivars both in the absence and in the presence of V. dahliae. These results showed that olive leaves of VWO-tolerant and VWO-susceptible cultivars adopt different strategies to cope with the pathogen.

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H ² > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB Model to Micrometeorology Environment in Begonia Fimbristipula Hance

The high quality and efficient production of greenhouse vegetation depend on micrometeorology environmental adjusting such as system warming and illumination supplement. In order to improve the quantity, quality, and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of the greenhouse environment and reduce the running costs. The parameters from the color information of the plant canopy reflect the internal physiological conditions, thus, the RGB model has been widely used in the color analysis of digital pictures of leaves. We take photographs of Begonia Fimbristipula Hance (BFH) growing in the greenhouse at a fixed time every day and measure the meteorological factors. The results showed that the color scale for the single leaf, single plant, and the populated canopy of the BFH photographs all have skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of the canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode, and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized. The models can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.

LED color gradient as a new screening tool for rapid phenotyping of plant responses to light quality

BACKGROUND

The increasing demand for local food production is fueling high interest in the development of controlled environment agriculture. In particular, LED technology brings energy-saving advantages together with the possibility of manipulating plant phenotypes through light quality control. However, optimizing light quality is required for each cultivated plant and specific purpose.

FINDINGS

This article shows that the combination of LED gradient set-ups with imaging-based non-destructive plant phenotyping constitutes an interesting new screening tool with the potential to improve speed, logistics, and information output. To validate this concept, an experiment was performed to evaluate the effects of a complete range of red:blue ratios on 7 plant species: Arabidopsis thaliana, Brachypodium distachyon, Euphorbia peplus, Ocimum basilicum, Oryza sativa, Solanum lycopersicum, and Setaria viridis. Plants were exposed during 30 days to the light gradient and showed significant, but species-dependent, responses in terms of dimension, shape, and color. A time-series analysis of phenotypic descriptors highlighted growth changes but also transient responses of plant shapes to the red:blue ratio.

CONCLUSION

This approach, which generated a large reusable dataset, can be adapted for addressing specific needs in crop production or fundamental questions in photobiology.

Verticillium Wilt of Olive and its Control: What Did We Learn during the Last Decade?

Verticillium (Verticillium dahliae Kleb.) wilt is one of the most devastating diseases affecting olive (Olea europaea L. subsp. europaea var. europaea) cultivation. Its effective control strongly relies on integrated management strategies. Olive cultivation systems are experiencing important changes (e.g., high-density orchards, etc.) aiming at improving productivity. The impact of these changes on soil biology and the incidence/severity of olive pests and diseases has not yet been sufficiently evaluated. A comprehensive understanding of the biology of the pathogen and its populations, the epidemiological factors contributing to exacerbating the disease, the underlying mechanisms of tolerance/resistance, and the involvement of the olive-associated microbiota in the tree's health is needed. This knowledge will be instrumental to developing more effective control measures to confront the disease in regions where the pathogen is present, or to exclude it from V. dahliae-free areas. This review compiles the most recent advances achieved to understand the olive-V. dahliae interaction as well as measures to control the disease. Aspects such as the molecular basis of the host-pathogen interaction, the identification of new biocontrol agents, the implementation of "-omics" approaches to unravel the basis of disease tolerance, and the utilization of remote sensing technology for the early detection of pathogen attacks are highlighted.

Quantifying Citrus Tree Health Using True Color UAV Images

Huanglongbing (HLB) and Phytophthora foot and root rot are diseases that affect citrus production and profitability. The symptoms and physiological changes associated with these diseases are diagnosed through expensive and time-consuming field measurements. Unmanned aerial vehicles (UAVs) using red/green/blue (RGB, true color) imaging, may be an economic alternative to diagnose diseases. A methodology using a UAV with a RGB camera was developed to assess citrus health. The UAV was flown in April 2018 on a grapefruit field infected with HLB and foot rot. Ten trees were selected for each of the following disease classifications: (HLB-, foot rot–), (HLB+, foot rot–), (HLB-, foot rot+) (HLB+, foot rot+). Triangular greenness index (TGI) images were correlated with field measurements such as tree nutritional status, leaf area, SPAD (leaf greenness), foot rot disease severity and HLB. It was found that 61% of the TGI differences could be explained by Na, Fe, foot rot, Ca, and K. This study shows that diseased citrus trees can be monitored using UAVs equipped with RGB cameras, and that TGI can be used to explain subtle differences in tree health caused by multiple diseases.