Induced plant volatiles allow sensitive monitoring of plant health status in greenhouses

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

New trends in methyl salicylate sensing and their implications in agriculture

Methyl salicylate (MeSal) is an organic compound present in plants during stress events and is therefore a key marker for early plant disease detection. It has usually been detected by conventional methods that require bulky and costly equipment, such as gas chromatography or mass spectrometry. Currently, however, chemical sensors provide an alternative for MeSal monitoring, showing good performance for its determination in the vapour or liquid phase. The most promising concepts used in MeSal determination include sensors based on electrochemical and conductometric principles, although other technologies based on mass-sensitive, microwave, or spectrophotometric principles also show promise. The receptor elements or sensitive materials are shown to be part of the key elements in these sensing technologies. A literature survey identified a significant contribution of bioreceptors, including enzymes, odourant-binding proteins or peptides, as well as receptors based on polymers or inorganic materials in MeSal determination. This work reviews these concepts and materials and discusses their future prospects and limitations for application in plant health monitoring.

Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor

Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.

Flooding and Herbivory Interact to Alter Volatile Organic Compound Emissions in Two Maize Hybrids

Flooding is a major plant abiotic stress factor that is frequently experienced by plants simultaneously with other biotic stresses, including herbivory. How plant volatile emissions, which mediate interactions with a wide range of organisms, are influenced by flooding and by multiple co-occurring stress factors remains largely unexplored. Using Spodoptera frugiperda (Lepidoptera: Noctuidae) (fall armyworm) as the insect pest and two maize (Zea mays, L. Poaceae) hybrids differentially marketed for conventional and organic production, we assessed the effects of flooding, herbivory, and both stress factors on the composition of blends of emitted volatiles. Headspace volatiles were collected from all treatment combinations seven days after flooding. We documented metrics indicative of biomass allocation to determine the effects of individual and combined stressors on plant growth. We also evaluated relationships between volatile emissions and indicators of soil chemical characteristics as influenced by treatment factors. Flooding and herbivory induced the emission of volatile organic compounds (VOCs) in similar ways on both maize hybrids, but the interaction of both stress factors produced significantly larger quantities of emitted volatiles. Thirty-eight volatile compounds were identified, including green leaf volatiles, monoterpenes, an aldehyde, a benzoate ester, sesquiterpenes, a diterpene alcohol, and alkane hydrocarbons. The hybrid marketed for organic production was a stronger VOC emitter. As expected, plant biomass was detrimentally affected by flooding. Soil chemical properties were less responsive to the treatment factors. Taken together, the results suggest that flooding stress and the interactions of flooding and insect attack can shape the emission of plant volatiles and further influence insect-plant interactions.

Plant pest surveillance: from satellites to molecules

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Trends and applications in plant volatile sampling and analysis

Volatile organic compounds (VOCs) released by plants serve as information and defense chemicals in mutualistic and antagonistic interactions and mitigate effects of abiotic stress. Passive and dynamic sampling techniques combined with gas chromatography-mass spectrometry analysis have become routine tools to measure emissions of VOCs and determine their various functions. More recently, knowledge of the roles of plant VOCs in the aboveground environment has led to the exploration of similar functions in the soil and rhizosphere. Moreover, VOC patterns have been recognized as sensitive and time-dependent markers of biotic and abiotic stress. This focused review addresses these developments by presenting recent progress in VOC sampling and analysis. We show advances in the use of small, inexpensive sampling devices and describe methods to monitor plant VOC emissions in the belowground environment. We further address latest trends in real-time measurements of volatilomes in plant phenotyping and most recent developments of small portable devices and VOC sensors for non-invasive VOC fingerprinting of plant disease. These technologies allow for innovative approaches to study plant VOC biology and application in agriculture.

A standardised bioassay method using a bench-top spray tower to evaluate entomopathogenic fungi for control of the greenhouse whitefly, Trialeurodes vaporariorum

BACKGROUND

Bioassays evaluating entomopathogenic fungi (EPF) isolates for effective microbial control of whitefly are a fundamental part of the screening process for bioprotectants, but development of repeatable, robust bioassays is not straightforward. Currently, there is no readily available standardised method to test the efficacy of EPF on whitefly. Here, we describe the calibration and use of a spray tower to deliver a standardised protocol to assess EPF activity; the method was validated using 18 EPF from four genera in tests against greenhouse whitefly, Trialeurodes vaporariorum (Westwood).

RESULTS

At 138 kPa, the sprayer delivered 0.062 mL mm^-2 (620 L ha^-1 ) and an even deposition of spray across the central 1590 mm² of the spray area. Average conidial deposition for all EPF was 252 conidia mm^-2 and equivalent to 2.5 × 10¹² conidia ha^-1 at an application concentration of 1 × 10⁷ conidia mL^-1 . Conidial deposition of a test Beauveria bassiana suspension increased with increasing application concentration. Egg laying by T. vaporariorum adults was restricted to 177 mm² using clip cages specifically designed to ensure that third-instar T. vaporariorum received a uniform spray coverage. Nymphs occupied 373 ± 5 mm² of the leaf after migrating during the first instar. Average T. vaporariorum mortality totaled 8-89% 14 days after application of 1 × 10⁷ conidia mL^-1 of each EPF isolate.

CONCLUSION

Combining the calibrated sprayer and bioassay method provides a reliable, standardised approach to test the virulence of EPF against whitefly nymphs. This laboratory-based assay is affordable, replicable and allows the user to alter the dose of conidia applied to the target.

Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles

Plant pathogen detection conventionally relies on molecular technology that is complicated, time-consuming and constrained to centralized laboratories. We developed a cost-effective smartphone-based volatile organic compound (VOC) fingerprinting platform that allows non-invasive diagnosis of late blight caused by Phytophthora infestans by monitoring characteristic leaf volatile emissions in the field. This handheld device integrates a disposable colourimetric sensor array consisting of plasmonic nanocolorants and chemo-responsive organic dyes to detect key plant volatiles at the ppm level within 1 min of reaction. We demonstrate the multiplexed detection and classification of ten individual plant volatiles with this field-portable VOC-sensing platform, which allows for early detection of tomato late blight 2 d after inoculation, and differentiation from other pathogens of tomato that lead to similar symptoms on tomato foliage. Furthermore, we demonstrate a detection accuracy of ≥95% in diagnosis of P. infestans in both laboratory-inoculated and field-collected tomato leaves in blind pilot tests. Finally, the sensor platform has been beta-tested for detection of P. infestans in symptomless tomato plants in the greenhouse setting.

Volatiles Emitted from Maize Ears Simultaneously Infected with Two Fusarium Species Mirror the Most Competitive Fungal Pathogen

Along with barley and rice, maize provides staple food for more than half of the world population. Maize ears are regularly infected with fungal pathogens of the Fusarium genus, which, besides reducing yield, also taint grains with toxic metabolites. In an earlier work, we have shown that maize ears infection with single Fusarium strains was detectable through volatile sensing. In nature, infection most commonly occurs with more than a single fungal strain; hence we tested how the interactions of two strains would modulate volatile emission from infected ears. For this purpose, ears of a hybrid and a dwarf maize variety were simultaneously infected with different strains of Fusarium graminearum and F. verticillioides and, the resulting volatile profiles were compared to the ones of ears infected with single strains. Disease severity, fungal biomass, and the concentration of the oxylipin 9-hydroxy octadecadienoic acid, a signaling molecule involved in plant defense, were monitored and correlated to volatile profiles. Our results demonstrate that in simultaneous infections of hybrid and dwarf maize, the most competitive fungal strains had the largest influence on the volatile profile of infected ears. In both concurrent and single inoculations, volatile profiles reflected disease severity. Additionally, the data further indicate that dwarf maize and hybrid maize might emit common (i.e., sesquiterpenoids) and specific markers upon fungal infection. Overall this suggests that volatile profiles might be a good proxy for disease severity regardless of the fungal competition taking place in maize ears. With the appropriate sensitivity and reliability, volatile sensing thus appears as a promising tool for detecting fungal infection of maize ears under field conditions.

Oviposition preference and larval performance of Epiphyas postvittana (Lepidoptera: Tortricidae) on Botrytis cinerea (Helotiales: Sclerotiniaceae) infected berries of Vitis vinifera (Vitales: Vitaceae)

In this paper we tested the behavior of gravid Epiphyas postvittana in selecting the most-appropriate site for oviposition thus benefitting offspring performance. Our hypothesis was built on Jaenike's preference-performance hypothesis (also referred to as the "mother-knows-the-best" hypothesis). To test this, we used the interacting Epiphyas postvittana, its host Vitis vinifera, and the pathogenic microbe Botrytis cinerea system. Populations of E. postvittana and B. cinerea often exist concurrently on V. vinifera in Australasia and their interaction and mutual influence are currently being explored, although the suggestion presently is that the relationship between E. postvittana and B. cinerea is mutualistic. We tested the effect of volatiles from B. cinerea-infected berries and uninfected (control) berries of V. vinifera on the oviposition behavior of E. postvittana. We also characterized the effects of B. cinerea infection on the berries of V. vinifera on the growth and development of E. postvittana. Contrary to the preference-performance hypothesis, oviposition choices made by gravid E. postvittana did not result in the best offspring survival, development, and performance. The preference for oviposition by E. postvittana was strongly influenced by the olfactory and tactile cues. She laid fewer eggs on B. cinerea-infected berries compared to uninfected berries of V. vinifera. The larvae of E. postvittana showed no preference to uninfected berries of V. vinifera. The larvae fed on B. cinerea-infected berries of V. vinifera showing greater survival rate, shorter time to pupation, greater pupal mass, and on becoming adults they laid more numbers of eggs than the larvae that were enabled to feed on uninfected berries. The larvae of E. postvittana transport the conidia of B. cinerea and transmit grey-mould disease to uninfected berries of V. vinifera.

Current and Prospective Methods for Plant Disease Detection

Food losses due to crop infections from pathogens such as bacteria, viruses and fungi are persistent issues in agriculture for centuries across the globe. In order to minimize the disease induced damage in crops during growth, harvest and postharvest processing, as well as to maximize productivity and ensure agricultural sustainability, advanced disease detection and prevention in crops are imperative. This paper reviews the direct and indirect disease identification methods currently used in agriculture. Laboratory-based techniques such as polymerase chain reaction (PCR), immunofluorescence (IF), fluorescence in-situ hybridization (FISH), enzyme-linked immunosorbent assay (ELISA), flow cytometry (FCM) and gas chromatography-mass spectrometry (GC-MS) are some of the direct detection methods. Indirect methods include thermography, fluorescence imaging and hyperspectral techniques. Finally, the review also provides a comprehensive overview of biosensors based on highly selective bio-recognition elements such as enzyme, antibody, DNA/RNA and bacteriophage as a new tool for the early identification of crop diseases.

Detection of Huanglongbing disease using differential mobility spectrometry

The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.

Detection of diseased plants by analysis of volatile organic compound emission

This review focuses on the detection of diseased plants by analysis of volatile organic compound (VOC) emissions. It includes an overview of studies that report on the impact of infectious and noninfectious diseases on these emissions and discusses the specificity of disease-induced emissions. The review also provides an overview of processes that affect the gas balance of plant volatiles, including their loss processes. These processes are considered as important because they contribute to the time-dynamic concentration profiles of plant-emitted volatiles. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on agricultural application. Dynamic sampling coupled with gas chromatography and followed by an appropriate detector is considered as the most appropriate method for application in agriculture. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in agricultural practice. However, to apply such an instrument in agriculture remains a challenge, mainly due to high costs.