Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease

Assembly and operation of an imaging system for long-term monitoring of bioluminescent and fluorescent reporters in plants

BACKGROUND

Non-invasive reporter systems are powerful tools to query physiological and transcriptional responses in organisms. For example, fluorescent and bioluminescent reporters have revolutionized cellular and organismal assays and have been used to study plant responses to abiotic and biotic stressors. Integrated, cooled charge-coupled device (CCD) camera systems have been developed to image bioluminescent and fluorescent signals in a variety of organisms; however, these integrated long-term imaging systems are expensive.

RESULTS

We have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions. This system combines environmental growth chambers with high-sensitivity CCD cameras, multi-wavelength LEDs, open-source software, and several options for coordinating lights with imaging. This easy-to-assemble system can be used for short and long-term imaging of bioluminescent reporters, acute light-response, circadian rhythms, delayed fluorescence, and fluorescent-protein-based assays in vivo.

CONCLUSIONS

We have developed two self-assembled imaging systems that will be useful to researchers interested in continuously monitoring in vivo reporter systems in various plant species.

Modelling cassava production and pest management under biotic and abiotic constraints

We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements

The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Cassava diseases caused by Xanthomonas phaseoli pv. manihotis and Xanthomonas cassavae

Xanthomonas phaseoli pv. manihotis (Xpm) and X. cassavae (Xc) are two bacterial pathogens attacking cassava. Cassava bacterial blight (CBB) is a systemic disease caused by Xpm, which might have dramatic effects on plant growth and crop production. Cassava bacterial necrosis is a nonvascular disease caused by Xc with foliar symptoms similar to CBB, but its impacts on the plant vigour and the crop are limited. In this review, we describe the epidemiology and ecology of the two pathogens, the impacts and management of the diseases, and the main research achievements for each pathosystem. Because Xc data are sparse, our main focus is on Xpm and CBB.

Identification of the Capsicum baccatum NLR Protein CbAR9 Conferring Disease Resistance to Anthracnose

Anthracnose is caused by Colletotrichum species and is one of the most virulent fungal diseases affecting chili pepper (Capsicum) yield globally. However, the noble genes conferring resistance to Colletotrichum species remain largely elusive. In this study, we identified CbAR9 as the causal locus underlying the large effect quantitative trait locus CcR9 from the anthracnose-resistant chili pepper variety PBC80. CbAR9 encodes a nucleotide-binding and leucine-rich repeat (NLR) protein related to defense-associated NLRs in several other plant species. CbAR9 transcript levels were induced dramatically after Colletotrichum capsici infection. To explore the biological function, we generated transgenic Nicotiana benthamiana lines overexpressing CbAR9, which showed enhanced resistance to C. capsici relative to wild-type plants. Transcript levels of pathogenesis-related (PR) genes increased markedly in CbAR9-overexpressing N. benthamiana plants. Moreover, resistance to anthracnose and transcript levels of PR1 and PR2 were markedly reduced in CbAR9-silenced chili pepper fruits after C. capsici infection. Our results revealed that CbAR9 contributes to innate immunity against C. capsici.

The Capsicum baccatum-Specific Truncated NLR Protein CbCN Enhances the Innate Immunity against Colletotrichum acutatum

Chili pepper (Capsicumannuum) is an important fruit and spice used globally, but its yield is seriously threatened by anthracnose. Capsicum baccatum is particularly valuable as it carries advantageous disease resistance genes. However, most of the genes remain to be identified. In this study, we identified the C. baccatum-specific gene CbCN, which encodes a truncated nucleotide-binding and leucine-rich repeat protein in the anthracnose resistant chili pepper variety PBC80. The transcription of CbCN was greater in PBC80 than it was in the susceptible variety An-S after Colletotrichum acutatum inoculation. In order to investigate the biological function of CbCN, we generated transgenic tobacco lines constitutively expressing CbCN. Notably, CbCN-overexpressing transgenic plants exhibited enhanced resistance to C. acutatum compared to wild-type plants. Moreover, the expression of pathogenesis-related (PR) genes was remarkably increased in a CbCN-overexpressing tobacco plants. In order to confirm these results in chili pepper, we silenced the CbCN gene using the virus-induced gene silencing system. The anthracnose resistance and expressions of PR1, PR2, and NPR1 were significantly reduced in CbCN-silenced chili peppers after C. acutatum inoculations. These results indicate that CbCN enhances the innate immunity against anthracnose caused by C. acutatum by regulating defense response genes.

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

Tackling microbial threats in agriculture with integrative imaging and computational approaches

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

Rapid identification of an Arabidopsis NLR gene as a candidate conferring susceptibility to Sclerotinia sclerotiorum using time-resolved automated phenotyping

The broad host range necrotrophic fungus Sclerotinia sclerotiorum is a devastating pathogen of many oil and vegetable crops. Plant genes conferring complete resistance against S. sclerotiorum have not been reported. Instead, plant populations challenged by S. sclerotiorum exhibit a continuum of partial resistance designated as quantitative disease resistance (QDR). Because of their complex interplay and their small phenotypic effect, the functional characterization of QDR genes remains limited. How broad host range necrotrophic fungi manipulate plant programmed cell death is for instance largely unknown. Here, we designed a time-resolved automated disease phenotyping pipeline enabling high-throughput disease lesion measurement with high resolution, low footprint at low cost. We could accurately recover contrasted disease responses in several pathosystems using this system. We used our phenotyping pipeline to assess the kinetics of disease symptoms caused by seven S. sclerotiorum isolates on six A. thaliana natural accessions with unprecedented resolution. Large effect polymorphisms common to the most resistant A. thaliana accessions identified highly divergent alleles of the nucleotide-binding site leucine-rich repeat gene LAZ5 in the resistant accessions Rubezhnoe and Lip-0. We show that impaired LAZ5 expression in laz5.1 mutant lines and in A. thaliana Rub natural accession correlate with enhanced QDR to S. sclerotiorum. These findings illustrate the value of time-resolved image-based phenotyping for unravelling the genetic bases of complex traits such as QDR. Our results suggest that S. sclerotiorum manipulates plant sphingolipid pathways guarded by LAZ5 to trigger programmed cell death and cause disease.

Xanthomonas diversity, virulence and plant-pathogen interactions

Xanthomonas spp. encompass a wide range of plant pathogens that use numerous virulence factors for pathogenicity and fitness in plant hosts. In this Review, we examine recent insights into host-pathogen co-evolution, diversity in Xanthomonas populations and host specificity of Xanthomonas spp. that have substantially improved our fundamental understanding of pathogen biology. We emphasize the virulence factors in xanthomonads, such as type III secreted effectors including transcription activator-like effectors, type II secretion systems, diversity resulting in host specificity, evolution of emerging strains, activation of susceptibility genes and strategies of host evasion. We summarize the genomic diversity in several Xanthomonas spp. and implications for disease outbreaks, management strategies and breeding for disease resistance.

Infection Assay for Xanthomonas campestris pv. campestris in Arabidopsis thaliana Mimicking Natural Entry via Hydathodes

Xanthomonas campestris pv. campestris (Xcc) causes the devastating disease Black rot in Brassicaceae. Typically Xcc enters the plant through specialized organs on the leaf margin, called hydathodes, and spreads from there through the vasculature. In order to mimic natural entry as closely as possible, we here describe a "hydathode guttation"-based entry assay for Xcc in Arabidopsis. This disease assay combines spray inoculation with the induction of guttation and allows reabsorption of guttation droplets by the plant. Moreover, our assay relies on a bioluminescent reporter strain of Xcc to allow direct visualization of both entry and subsequent spreading of Xcc in its host. The assay allows the routine infection from one to two hydathodes per Arabidopsis leaf. Infections are scored 14 days post inoculation, just before the infection goes systemic.

Breeding to adapt agriculture to climate change: affordable phenotyping solutions

Breeding is one of the central pillars of adaptation of crops to climate change. However, phenotyping is a key bottleneck that is limiting breeding efficiency. The awareness of phenotyping as a breeding limitation is not only sustained by the lack of adequate approaches, but also by the perception that phenotyping is an expensive activity. Phenotyping is not just dependent on the choice of appropriate traits and tools (e.g. sensors) but relies on how these tools are deployed on their carrying platforms, the speed and volume of data extraction and analysis (throughput), the handling of spatial variability and characterization of environmental conditions, and finally how all the information is integrated and processed. Affordable high throughput phenotyping aims to achieve reasonably priced solutions for all the components comprising the phenotyping pipeline. This mini-review will cover current and imminent solutions for all these components, from the increasing use of conventional digital RGB cameras, within the category of sensors, to open-access cloud-structured data processing and the use of smartphones. Emphasis will be placed on field phenotyping, which is really the main application for day-to-day phenotyping.

Pseudomonas syringae pv. tomato DC3000 polymutants deploying coronatine and two type III effectors produce quantifiable chlorotic spots from individual bacterial colonies in Nicotiana benthamiana leaves

Primary virulence factors of Pseudomonas syringae pv. tomato DC3000 include the phytotoxin coronatine (COR) and a repertoire of 29 effector proteins injected into plant cells by the type III secretion system (T3SS). DC3000 derivatives differentially producing COR, the T3SS machinery and subsets of key effectors were constructed and assayed in leaves of Nicotiana benthamiana. Bacteria were inoculated by the dipping of whole plants and assayed for population growth and the production of chlorotic spots on leaves. The strains fell into three classes. Class I strains are T3SS+ but functionally effectorless, grow poorly in planta and produce faint chlorotic spots only if COR+ . Class II strains are T3SS- or, if T3SS+ , also produce effectors AvrPtoB and HopM1. Class II strains grow better than class I strains in planta and, if COR+ , produce robust chlorotic spots. Class III strains are T3SS+ and minimally produce AvrPtoB, HopM1 and three other effectors encoded in the P. syringae conserved effector locus. These strains differ from class II strains in growing better in planta, and produce chlorotic spots without COR if the precursor coronafacic acid is produced. Assays for chlorotic spot formation, in conjunction with pressure infiltration of low-level inoculum and confocal microscopy of fluorescent protein-labelled bacteria, revealed that single bacteria in the apoplast are capable of producing colonies and associated leaf spots in a 1 : 1 : 1 manner. However, COR makes no significant contribution to the bacterial colonization of the apoplast, but, instead, enables a gratuitous, semi-quantitative, surface indicator of bacterial growth, which is determined by the strain's effector composition.

Raspberry Pi-powered imaging for plant phenotyping

PREMISE OF THE STUDY

Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.

METHODS AND RESULTS

We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV.

CONCLUSIONS

This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

Raspberry Pi-powered imaging for plant phenotyping

PREMISE OF THE STUDY

Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.

METHODS AND RESULTS

We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV.

CONCLUSIONS

This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

The role of type III effectors from Xanthomonas axonopodis pv. manihotis in virulence and suppression of plant immunity

Xanthomonas axonopodis pv. manihotis (Xam) causes cassava bacterial blight, the most important bacterial disease of cassava. Xam, like other Xanthomonas species, requires type III effectors (T3Es) for maximal virulence. Xam strain CIO151 possesses 17 predicted T3Es belonging to the Xanthomonas outer protein (Xop) class. This work aimed to characterize nine Xop effectors present in Xam CIO151 for their role in virulence and modulation of plant immunity. Our findings demonstrate the importance of XopZ, XopX, XopAO1 and AvrBs2 for full virulence, as well as a redundant function in virulence between XopN and XopQ in susceptible cassava plants. We tested their role in pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) using heterologous systems. AvrBs2, XopR and XopAO1 are capable of suppressing PTI. ETI suppression activity was only detected for XopE4 and XopAO1. These results demonstrate the overall importance and diversity in functions of major virulence effectors AvrBs2 and XopAO1 in Xam during cassava infection.

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

Una de las preguntas más desafiantes del fitomejoramiento y de la fitopatología molecular es ¿cuáles son las bases genéticas y moleculares de la resistencia cuantitativa a enfermedades?. El escaso conocimiento de cómo este tipo de resistencia funciona ha obstaculizado que los fitomejoradores la aprovecharlo plenamente. Para superar estos obstáculos se han desarrollado nuevas metodologías para el estudio de rasgos cuantitativos. Los enfoques como el mapeo genético, la identificación de loci de rasgos cuantitativos (QTL) y el mapeo por asociaciones, incluyendo el enfoque de genes candidatos y los estudios de asociación amplia del genoma, se han llevado a cabo históricamente para describir rasgos cuantitativos y por lo tanto para estudiar QDR. Además, se han proporcionado grandes avances en la obtención de datos fenotípicos cuantitativos para mejorar estos análisis. Recientemente, algunos genes asociados a QDR han sido clonados, lo que conduce a nuevas hipótesis sobre las bases moleculares de este tipo de resistencia. En esta revisión presentamos los avances más recientes sobre QDR y la correspondiente aplicación, que han permitido postular nuevas ideas que pueden ayudar a construir nuevos modelos. Algunas de las hipótesis presentadas aquí como posibles explicaciones para QDR están relacionadas con el nivel de expresión y el splicing alternativo de algunos genes relacionados con la defensa, la acción de "alelos débiles" de genes R, la presencia de variantes alélicas en los genes implicados en la respuesta de defensa y un papel central de quinasas o pseudoqinasas. Con la información recapitulada en esta revisión es posible concluir que la distinción conceptual entre resistencia cualitativa y cuantitativa puede ser cuestionada ya que ambos comparten importantes componentes.

The genome of the cotton bacterial blight pathogen Xanthomonas citri pv. malvacearum strain MSCT1

Xanthomonas citri pv. malvacearum is a major pathogen of cotton, Gossypium hirsutum L.. In this study we report the complete genome of the X. citri pv. malvacearum strain MSCT1 assembled from long read DNA sequencing technology. The MSCT1 genome is the first X. citri pv. malvacearum genome with complete coding regions for X. citri pv. malvacearum transcriptional activator-like effectors. In addition functional and structural annotations are presented in this study that will provide a foundation for future pathogenesis studies with MSCT1.

Foundational and Translational Research Opportunities to Improve Plant Health

Reader Comments | Submit a Comment The white paper reports the deliberations of a workshop focused on biotic challenges to plant health held in Washington, D.C. in September 2016. Ensuring health of food plants is critical to maintaining the quality and productivity of crops and for sustenance of the rapidly growing human population. There is a close linkage between food security and societal stability; however, global food security is threatened by the vulnerability of our agricultural systems to numerous pests, pathogens, weeds, and environmental stresses. These threats are aggravated by climate change, the globalization of agriculture, and an over-reliance on nonsustainable inputs. New analytical and computational technologies are providing unprecedented resolution at a variety of molecular, cellular, organismal, and population scales for crop plants as well as pathogens, pests, beneficial microbes, and weeds. It is now possible to both characterize useful or deleterious variation as well as precisely manipulate it. Data-driven, informed decisions based on knowledge of the variation of biotic challenges and of natural and synthetic variation in crop plants will enable deployment of durable interventions throughout the world. These should be integral, dynamic components of agricultural strategies for sustainable agriculture.