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

Can nanotechnology and genomics innovations trigger agricultural revolution and sustainable development?

At the dawn of new millennium, policy makers and researchers focused on sustainable agricultural growth, aiming for food security and enhanced food quality. Several emerging scientific innovations hold the promise to meet the future challenges. Nanotechnology presents a promising avenue to tackle the diverse challenges in agriculture. By leveraging nanomaterials, including nano fertilizers, pesticides, and sensors, it provides targeted delivery methods, enhancing efficacy in both crop production and protection. This integration of nanotechnology with agriculture introduces innovations like disease diagnostics, improved nutrient uptake in plants, and advanced delivery systems for agrochemicals. These precision-based approaches not only optimize resource utilization but also reduce environmental impact, aligning well with sustainability objectives. Concurrently, genetic innovations, including genome editing and advanced breeding techniques, enable the development of crops with improved yield, resilience, and nutritional content. The emergence of precision gene-editing technologies, exemplified by CRISPR/Cas9, can transform the realm of genetic modification and enabled precise manipulation of plant genomes while avoiding the incorporation of external DNAs. Integration of nanotechnology and genetic innovations in agriculture presents a transformative approach. Leveraging nanoparticles for targeted genetic modifications, nanosensors for early plant health monitoring, and precision nanomaterials for controlled delivery of inputs offers a sustainable pathway towards enhanced crop productivity, resource efficiency, and food safety throughout the agricultural lifecycle. This comprehensive review outlines the pivotal role of nanotechnology in precision agriculture, emphasizing soil health improvement, stress resilience against biotic and abiotic factors, environmental sustainability, and genetic engineering.

A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform

Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research.

Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400 nm) and plant defense hormone-mediated responses (750 nm) rather than changes in leaf pigments or internal structure (800-900 nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.

A review on nanomaterial-based SERS substrates for sustainable agriculture

The agricultural sector plays a pivotal role in driving the economy of many developing countries. Any dent in this economical structure may have a severe impact on a country's population. With rising climate change and increasing pollution, the agricultural sector is experiencing significant damage. Over time this cumulative damage will affect the integrity of food crops and create food security issues around the world. Therefore, an early warning system is needed to detect possible stress on food crops. Here we present a review of the recent developments in nanomaterial-based Surface Enhanced Raman Spectroscopy (SERS) substrates which could be utilized to monitor agricultural crop responses to natural and anthropogenic stress. Initially, our review delves into diverse and cost-effective strategies for fabricating SERS substrates, emphasizing their intelligent utilization across various agricultural scenarios. In the second phase of our review, we spotlight the specific application of SERS in addressing critical food security issues. By detecting nutrients, hormones, and effector molecules in plants, SERS provides valuable insights into plant health. Furthermore, our exploration extends to the detection of contaminants, chemicals, and foodborne pathogens within plants, showcasing the versatility of SERS in ensuring food safety. The cumulative knowledge derived from these discussions illustrates the transformative potential of SERS in bolstering the agricultural economy. By enhancing precision in nutrient management, monitoring plant health, and enabling rapid detection of harmful substances, SERS emerges as a pivotal tool in promoting sustainable and secure agricultural practices. Its integration into agricultural processes not only augments productivity but also establishes a robust defence against potential threats to crop yield and food quality. As SERS continues to evolve, its role in shaping the future of agriculture becomes increasingly pronounced, promising a paradigm shift in how we approach and address challenges in food production and safety.

Early and high-throughput plant diagnostics: strategies for disease detection

The rising global occurrence of plant pathogens highlights the need for a thorough reassessment of current disease detection and management schemes. To that end, we review the utility and limitations of the available sensing platforms deployed for phytodiagnostics in the field. We also discuss recent advances in the use of broad-spectrum biomarkers such as phytohormones and volatile organic compounds (VOCs), and assess the feasibility of deploying these platforms on a large scale. Because these platforms are often complementary, we propose a compressed sensing approach that combines several sensing platforms to manage plant pathogens while minimizing additional costs. Finally, we provide an outlook for the potential benefits of integrating new sensing technologies into farming for timely interventions.

Metal Halide Perovskite Nanocrystals-Intermediated Hydrogel for Boosting the Biosensing Performance

Metal-halide perovskites have become attractive nanomaterials for advanced biosensors, yet the structural design remains challenging due to the trade-off between environmental stability and sensing sensitivity. Herein, a trinity strategy is proposed to address this issue by integrating Mn (II) substitution with CsPb2Cl5 inert shell and NH2-PEG-COOH coating for designing Mn2+-doped CsPbCl3/CsPb2Cl5 core/shell hetero perovskite nanocrystals (PMCP PNCs). The trinity strategy isolates the emissive Mn2+-doped CsPbCl3 core from water and the Mn2+ d-d transition generates photoluminescence with a long lifetime, endowing the NH2-PEG-COOH capped Mn2+-doped CsPbCl3/CsPb2Cl5 PNCs with robust water stability and oxygen-sensitive property. Given the structural integration, photoluminescent hydrogel biosensors are designed by embedding the PMCP PNCs into the hydrogel system to deliver on-site pesticide information on food products. Impressively, benefiting from the dual enzyme triggered-responsive property of PMCP PNCs, the hydrogel biosensor is endowed with ultra-high sensitivity toward chlorpyrifos pesticide at the nanogram per milliliter level. Such a robust PMCP PNCs-based hydrogel sensor can provide accurate pesticide information while guiding the construction of photoluminescent biosensors for upcoming on-site applications.

Portable hydrogel kit based on Michael addition reaction for (E)-2-hexenal gas detection

Plants exhibit rapid responses to biotic and abiotic stresses by releasing a range of volatile organic compounds (VOCs). Monitoring changes in these VOCs holds the potential for the early detection of plant diseases. This study proposes a method for identifying late blight in potatoes based on the detection of (E)-2-hexenal, one of the major VOC markers released during plant infection by Phytophthora infestans. By combining the Michael addition reaction with cysteine-mediated etching of aggregation-induced emission gold nanoclusters (Au NCs), we have developed a portable hydrogel kit for on-site detection of (E)-2-hexenal. The Michael addition reaction between (E)-2-hexenal and cysteine effectively alleviates the etching of cysteine-mediated Au NCs, leading to a distinct fluorescence color change in the Au NCs, enabling a detection limit of 0.61 ppm. Utilizing the superior loading and diffusion characteristics of the three-dimensional structure of agarose hydrogel, our sensor demonstrated exceptional performance in terms of sensitivity, selectivity, reaction time, and ease of use. Moreover, quantitative measurement of (E)-2-hexenal was made easier by using ImageJ software to transform fluorescent images from the hydrogel kit into digital data. Such method was effectively used for the early detection of potato late blight. This study presents a low-cost, portable fluorescent analytical tool, offering a new avenue for on-site detection of plant diseases.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts

Phenylketonuria (PKU) is one of the most common genetic metabolic diseases, especially among newborns. Traditional clinical examination of newborn blood samples for PKU is invasive, laborious, and limited to hospitals and healthcare facilities. We reported herein a SERS-based sensor array with three thiophenolic nanoreceptors built on a patterned nanorod vertical array for rapid and inexpensive detection of characteristic volatile biomarkers indicative of PKU in the urine and accurate classification of newborn baby patients all performed on a hand-held SERS spectrophotometer. The well-ordered array was generated from the volatility-driven assembly of gold nanorods (AuNRs) into an upright and closely packed hexagonal configuration. The uniformly distributed nanowells between AuNRs offered an intense and aspect-ratio-dependent plasmonic field for the molecular enhancement of SERS outputs. The SERS-based detector was integrated into a test chip for regular monitoring of volatile phenylketone bodies in the spiked solution or patients' urine within 5 min, allowing the quantification of a wide variety of normal or abnormal metabolites at their physiologically relevant concentration range. The detection limits for common biomarkers of PKU, including phenylpyruvic acid, 4-hydroxyphenylacetic acid, and phenylacetic acid, were at a few μM and well below the diagnostic thresholds. Moreover, the volatile headspace mixtures from a given urine sample could be fingerprinted by the sensor array and discriminated using machine-learning algorithms. Ultimately, the discrimination of baby patients among 26 cases of mild and classic PKU phenotypes and 17 cases of healthy volunteers could be realized with an overall accuracy of 97%. This hand-held SERS platform plays a pivotal role in advancing healthcare applications in quick screening of neonatal PKU through a facile urinary vapor test.

Plant Molecular Phenology and Climate Feedbacks Mediated by BVOCs

Climate change profoundly affects the timing of seasonal activities of organisms, known as phenology. The impact of climate change is not unidirectional; it is also influenced by plant phenology as plants modify atmospheric composition and climatic processes. One important aspect of this interaction is the emission of biogenic volatile organic compounds (BVOCs), which link the Earth's surface, atmosphere, and climate. BVOC emissions exhibit significant diurnal and seasonal variations and are therefore considered essential phenological traits. To understand the dynamic equilibrium arising from the interplay between plant phenology and climate, this review presents recent advances in comprehending the molecular mechanisms underpinning plant phenology and its interaction with climate. We provide an overview of studies investigating molecular phenology, genome-wide gene expression analyses conducted in natural environments, and how these studies revolutionize the concept of phenology, shifting it from observable traits to dynamic molecular responses driven by gene-environment interactions. We explain how this knowledge can be scaled up to encompass plant populations, regions, and even the globe by establishing connections between molecular phenology, changes in plant distribution, species composition, and climate.

Extrusion Printing of Surface-Functionalized Metal-Organic Framework Inks for a High-Performance Wearable Volatile Organic Compound Sensor

Wearable sensors hold immense potential for real-time and non-destructive sensing of volatile organic compounds (VOCs), requiring both efficient sensing performance and robust mechanical properties. However, conventional colorimetric sensor arrays, acting as artificial olfactory systems for highly selective VOC profiling, often fail to meet these requirements simultaneously. Here, a high-performance wearable sensor array for VOC visual detection is proposed by extrusion printing of hybrid inks containing surface-functionalized sensing materials. Surface-modified hydrophobic polydimethylsiloxane (PDMS) improves the humidity resistance and VOC sensitivity of PDMS-coated dye/metal-organic frameworks (MOFs) composites. It also enhances their dispersion within liquid PDMS matrix, thereby promoting the hybrid liquid as high-quality extrusion-printing inks. The inks enable direct and precise printing on diverse substrates, forming a uniform and high particle-loading (70 wt%) film. The printed film on a flexible PDMS substrate demonstrates satisfactory flexibility and stretchability while retaining excellent sensing performance from dye/MOFs@PDMS particles. Further, the printed sensor array exhibits enhanced sensitivity to sub-ppm VOC levels, remarkable resistance to high relative humidity (RH) of 90%, and the differentiation ability for eight distinct VOCs. Finally, the wearable sensor proves practical by in situ monitoring of wheat scab-related VOC biomarkers. This study presents a versatile strategy for designing effective wearable gas sensors with widespread applications.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.

Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach

Crop diseases pose significant threats to agriculture, impacting crop production. Biotic factors contribute to various diseases, including fungal, bacterial, and viral infections. Recent advancements in deep learning present a novel approach to the detection and recognition of these crop diseases. While considerable research has focused on identifying and recognizing crop diseases, fungal disease-affected crops have received relatively less attention and also detecting disease on different region datasets. This paper is about spotting fungal diseases in crops across different regions with diverse climates. It emphasizes the need for tailored detection methods, addressing the risk of mycotoxin production by fungi, which can harm both humans and animals. Detecting fungal diseases in apple, guava, and custard apple crops such as spot, scab, rust, rot, leaf spot, and insect ate. In the proposed work, the modified ResNeXt variant of the convolution neural network (CNN) technique was employed to predict 3 major crop classes of fungal disease. Initially, using Inception-v7 and ResNet for fungal disease in crops did not yield satisfactory results. A modified ResNeXt CNN model was proposed, showing improved fungal disease prediction. The novel model underwent a comparison with established methodologies. The suggested model draws upon a benchmark dataset consisting of 14,408 images capturing fungal diseases, categorized into three distinct classes: apple, custard apple, and guava. Experimental outcomes show that the proposed mutated ResNeXt model outperformed the state-of-the-art approaches. The model achieved 98.92% accuracy and high performance across recall, precision, and F1-score (above 99%) for the benchmark dataset, which gained encouragement and was comparable with the state-of-the-art approach.

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.

Flexible wearable sensors for crop monitoring: a review

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

An efficient deep learning model for tomato disease detection

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR's self-attention mechanism to craft a backbone feature extraction network, enhancing the model's capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network's ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

Enantioselective Detection of Gaseous Odorants with Peptide-Graphene Sensors Operating in Humid Environments

Replicating the sense of smell presents an ongoing challenge in the development of biomimetic devices. Olfactory receptors exhibit remarkable discriminatory abilities, including the enantioselective detection of individual odorant molecules. Graphene has emerged as a promising material for biomimetic electronic devices due to its unique electrical properties and exceptional sensitivity. However, the efficient detection of nonpolar odor molecules using transistor-based graphene sensors in a gas phase in environmental conditions remains challenging due to high sensitivity to water vapor. This limitation has impeded the practical development of gas-phase graphene odor sensors capable of selective detection, particularly in humid environments. In this study, we address this challenge by introducing peptide-functionalized graphene sensors that effectively mitigate undesired responses to changes in humidity. Additionally, we demonstrate the significant role of humidity in facilitating the selective detection of odorant molecules by the peptides. These peptides, designed to mimic a fruit fly olfactory receptor, spontaneously assemble into a monomolecular layer on graphene, enabling precise and specific odorant detection. The developed sensors exhibit notable enantioselectivity, achieving a remarkable 35-fold signal contrast between d- and l-limonene. Furthermore, these sensors display distinct responses to various other biogenic volatile organic compounds, demonstrating their versatility as robust tools for odor detection. By acting as both a bioprobe and an electrical signal amplifier, the peptide layer represents a novel and effective strategy to achieve selective odorant detection under normal atmospheric conditions using graphene sensors. This study offers valuable insights into the development of practical odor-sensing technologies with potential applications in diverse fields.

SERS-Based Hydrogen Bonding Induction Strategy for Gaseous Acetic Acid Capture and Detection

Surface-enhanced Raman scattering (SERS) can overcome the existing technological limitations, such as complex processes and harsh conditions in gaseous small-molecule detection, and advance the development of real-time gas sensing at room temperature. In this study, a SERS-based hydrogen bonding induction strategy for capturing and sensing gaseous acetic acid is proposed for the detection demands of gaseous acetic acid. This addresses the challenges of low adsorption of gaseous small molecules on SERS substrates and small Raman scattering cross sections and enables the first SERS-based detection of gaseous acetic acid by a portable Raman spectrometer. To provide abundant hydrogen bond donors and acceptors, 4-mercaptobenzoic acid (4-MBA) was used as a ligand molecule modified on the SERS substrate. Furthermore, a sensing chip with a low relative standard deviation (RSD) of 4.15% was constructed, ensuring highly sensitive and reliable detection. The hydrogen bond-induced acetic acid trapping was confirmed by experimental spectroscopy and density functional theory (DFT). In addition, to achieve superior accuracy compared to conventional methods, an innovative analytical method based on direct response hydrogen bond formation (IO-H/Iref) was proposed, enabling the detection of gaseous acetic acid at concentrations as low as 60 ppb. The strategy demonstrated a superior anti-interference capability in simulated breath and wine detection systems. Moreover, the high reusability of the chip highlights the significant potential for real-time sensing of gaseous acetic acid.

Gas-responsive two-dimensional metal-organic framework composites for trace visualization of volatile organic compounds

Highly sensitive and specific identification of complex volatile organic compound mixtures has always been a huge challenge in the field of gas detection. To address this issue, the gas-responsive two-dimensional metal-organic framework (MOF) composites have been designed for fabricating a colorimetric sensor arrays for extremely sensitive detection of volatile organic compounds (VOCs). The physically exfoliated MOF nanosheets Zn2(bim)4 with large surface area and abundant unsaturated active sites were used for loading various dyes to form dye/Zn2(bim)4 composites. Due to the protective effect on dye activity and preconcentration for VOCs, the dye/Zn2(bim)4 composites-based colorimetric sensor arrays showed significantly enhanced sensitivity compared with the corresponding dyes for the detection of various VOCs. The mechanical flexibility of the dye/MOF nanosheets endowed the excellent film-forming properties on various substrates for fabricating the colorimetric sensor arrays. Besides owing to the hydrophobic property and the protection of the Zn2(bim)4 nanosheets, the dye/Zn2(bim)4 sensor arrays exhibited excellent anti-interference including humidity and temperature influence. On the basis of the fantastic properties of dye/Zn2(bim)4 composites for VOCs detection, the dye/Zn2(bim)4 sensor arrays were applied for the early perception of the plant disease late blight via ultra-sensitive and highly specific sensing the VOCs released from the infected plants.

Wearable electrochemical sensors for plant small-molecule detection

Small molecules in plants - such as metabolites, phytohormones, reactive oxygen species (ROS), and inorganic ions - participate in the processes of plant growth and development, physiological metabolism, and stress response. Wearable electrochemical sensors, known for their fast response, high sensitivity, and minimal plant damage, serve as ideal tools for dynamically tracking these small molecules. Such sensors provide producers or agricultural researchers with noninvasive or minimally invasive means of obtaining plant signals. In this review we explore the applications of wearable electrochemical sensors in detecting plant small molecules, enabling scientific assessment of plant conditions, quantification of environmental stresses, and facilitation of plant health monitoring and disease prediction.

Next-generation methods for early disease detection in crops

Plant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. In this review, the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations are presented and discussed. Traditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme-linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high-throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non-cultivable pathogens in the laboratory, nucleic acid-based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last-generation portable thermocyclers may provide rapid detection results in situ. The advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Mercury-Mediated Epitaxial Accumulation of Au Atoms for Stained Hydrogel-Improved On-Site Mercury Monitoring

Trivalent Au ions are easily reduced to be zerovalent atoms by coexisting reductant reagents, resulting in the subsequent accumulation of Au atoms and formation of plasmonic nanostructures. In the absence of stabilizers or presence of weak stabilizers, aggregative growth of Au nanoparticles (NPs) always occurs, and unregular multidimensional Au materials are consequently constructed. Herein, the addition of nanomole-level mercury ions can efficiently prevent the epitaxial accumulation of Au atoms, and separated Au NPs with mediated morphologies and superior plasmonic characteristics are obtained. Experimental results and theoretical simulation demonstrate the Hg-concentration-reliant formation of plasmonic nanostructures with their mediated sizes and shapes in the presence of weak reductants. Moreover, the sensitive plasmonic responses of reaction systems exhibit selectivity comparable to that of Hg species. As a concept of proof, polymeric carbon dots (CDs) were used as the initial reductant, and the reactions between trivalent Au and CDs were studies. Significantly, Hg atoms prevent the epitaxial accumulation of Au atoms, and plasmonic NPs with decreased sizes were in situ synthesized, corresponding to varied surface plasmonic resonance absorption performance of the CD-induced hybrids. Moreover, with the integration of sensing substrates of CD-doped hydrogels, superior response stabilities, analysis selectivity, and sensitivity of Hg2+ ions were achieved on the basis of the mercury-mediated in situ chemical reactions between trivalent Au ions and reductant CDs. Consequently, a high-performance sensing strategy with the use of Au NP-staining hydrogels (nanostaining hydrogels) was exhibited. In addition to Hg sensing, the nanostaining hydrogels facilitated by doping of emerging materials and advanced chem/biostrategies can be developed as high-performance on-site monitoring routes to various pollutant species.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

Novel plant disease detection techniques-a brief review

Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

Plant Virus Sensor for the Rapid Detection of Bean Pod Mottle Virus Using Virus-Specific Nanocavities

This study presents a miniaturized sensor for rapid, selective, and sensitive detection of bean pod mottle virus (BPMV) in soybean plants. The sensor employs molecularly imprinted polymer technology to generate BPMV-specific nanocavities in porous polypyrrole. Leveraging the porous structure, high surface reactivity, and electron transfer properties of polypyrrole, the sensor achieves a sensitivity of 143 μA ng-1 mL cm-2, a concentration range of 0.01-100,000 ng/mL, a detection time of less than 2 min, and a detection limit of 41 pg/mL. These capabilities outperform those of conventional methods, such as enzyme-linked immunosorbent assays and reverse transcription polymerase chain reactions. The sensor possesses the ability to distinguish BPMV-infected soybean plants from noninfected ones while rapidly quantifying virus levels. Moreover, it can reveal the spatial distribution of virus concentration across distinct leaves, a capability not previously attained by cost-effective sensors for such detailed viral data within a plant. The BPMV-specific nanocavities can also be easily restored and reactivated for multiple uses through a simple wash with acetic acid. While MIP-based sensors for plant virus detection have been relatively understudied, our findings demonstrate their potential as portable, on-site diagnostic tools that avoid complex and time-consuming sample preparation procedures. This advancement addresses a critical need in plant virology, enhancing the detection and management of plant viral diseases.

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip

Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.

MOF-Based Chemiresistive Gas Sensors: Toward New Functionalities

The sensing performances of gas sensors must be improved and diversified to enhance quality of life by ensuring health, safety, and convenience. Metal-organic frameworks (MOFs), which exhibit an extremely high surface area, abundant porosity, and unique surface chemistry, provide a promising framework for facilitating gas-sensor innovations. Enhanced understanding of conduction mechanisms of MOFs has facilitated their use as gas-sensing materials, and various types of MOFs have been developed by examining the compositional and morphological dependences and implementing catalyst incorporation and light activation. Owing to their inherent separation and absorption properties and catalytic activity, MOFs are applied as molecular sieves, absorptive filtering layers, and heterogeneous catalysts. In addition, oxide- or carbon-based sensing materials with complex structures or catalytic composites can be derived by the appropriate post-treatment of MOFs. This review discusses the effective techniques to design optimal MOFs, in terms of computational screening and synthesis methods. Moreover, the mechanisms through which the distinctive functionalities of MOFs as sensing materials, heterostructures, and derivatives can be incorporated in gas-sensor applications are presented.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Low-rate smartphone videoscopy for microsecond luminescence lifetime imaging with machine learning

Time-resolved techniques have been widely used in time-gated and luminescence lifetime imaging. However, traditional time-resolved systems require expensive lab equipment such as high-speed excitation sources and detectors or complicated mechanical choppers to achieve high repetition rates. Here, we present a cost-effective and miniaturized smartphone lifetime imaging system integrated with a pulsed ultraviolet (UV) light-emitting diode (LED) for 2D luminescence lifetime imaging using a videoscopy-based virtual chopper (V-chopper) mechanism combined with machine learning. The V-chopper method generates a series of time-delayed images between excitation pulses and smartphone gating so that the luminescence lifetime can be measured at each pixel using a relatively low acquisition frame rate (e.g. 30 frames per second [fps]) without the need for excitation synchronization. Europium (Eu) complex dyes with different luminescent lifetimes ranging from microseconds to seconds were used to demonstrate and evaluate the principle of V-chopper on a 3D-printed smartphone microscopy platform. A convolutional neural network (CNN) model was developed to automatically distinguish the gated images in different decay cycles with an accuracy of >99.5%. The current smartphone V-chopper system can detect lifetime down to ∼75 µs utilizing the default phase shift between the smartphone video rate and excitation pulses and in principle can detect much shorter lifetimes by accurately programming the time delay. This V-chopper methodology has eliminated the need for the expensive and complicated instruments used in traditional time-resolved detection and can greatly expand the applications of time-resolved lifetime technologies.

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.

Hybrid Autofluorescence and Optoacoustic Microscopy for the Label-Free, Early and Rapid Detection of Pathogenic Infections in Vegetative Tissues

Agriculture plays a pivotal role in food security and food security is challenged by pests and pathogens. Due to these challenges, the yields and quality of agricultural production are reduced and, in response, restrictions in the trade of plant products are applied. Governments have collaborated to establish robust phytosanitary measures, promote disease surveillance, and invest in research and development to mitigate the impact on food security. Classic as well as modernized tools for disease diagnosis and pathogen surveillance do exist, but most of these are time-consuming, laborious, or are less sensitive. To that end, we propose the innovative application of a hybrid imaging approach through the combination of confocal fluorescence and optoacoustic imaging microscopy. This has allowed us to non-destructively detect the physiological changes that occur in plant tissues as a result of a pathogen-induced interaction well before visual symptoms occur. When broccoli leaves were artificially infected with Xanthomonas campestris pv. campestris (Xcc), eventually causing an economically important bacterial disease, the induced optical absorption alterations could be detected at very early stages of infection. Therefore, this innovative microscopy approach was positively utilized to detect the disease caused by a plant pathogen, showing that it can also be employed to detect quarantine pathogens such as Xylella fastidiosa.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping

The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.

Modern analytical and bioanalytical technologies and concepts for smart and precision farming

Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.

A MEMS-enabled portable gas chromatography injection system for trace analysis

Growing concerns about environmental conditions, public health, and disease diagnostics have led to the rapid development of portable sampling techniques to characterize trace-level volatile organic compounds (VOCs) from various sources. A MEMS-based micropreconcentrator (μPC) is one such approach that drastically reduces the size, weight, and power constraints offering greater sampling flexibility in many applications. However, the adoption of μPCs on a commercial scale is hindered by a lack of thermal desorption units (TDUs) that easily integrate μPCs with gas chromatography (GC) systems equipped with a flame ionization detector (FID) or a mass spectrometer (MS). Here, we report a highly versatile μPC-based, single-stage autosampler-injection unit for traditional, portable, and micro-GCs. The system uses μPCs packaged in 3D-printed swappable cartridges and is based on a highly modular interfacing architecture that allows easy-to-remove, gas-tight fluidic, and detachable electrical connections (FEMI). This study describes the FEMI architecture and demonstrates the FEMI-Autosampler (FEMI-AS) prototype (9.5 cm × 10 cm x 20 cm, ≈500 gms). The system was integrated with GC-FID, and the performance was investigated using synthetic gas samples and ambient air. The results were contrasted with the sorbent tube sampling technique using TD-GC-MS. FEMI-AS could generate sharp injection plugs (≈240 ms) and detect analytes with concentrations <15 ppb within 20 s and <100 ppt within 20 min of sampling time. With more than 30 detected trace-level compounds from ambient air, the demonstrated FEMI-AS, and the FEMI architecture significantly accelerate the adoption of μPCs on a broader scale.

A hybrid multifunctional physicochemical sensor suite for continuous monitoring of crop health

This work reports a first-of-its-kind hybrid wearable physicochemical sensor suite that we call PlantFit for simultaneous measurement of two key phytohormones, salicylic acid, and ethylene, along with vapor pressure deficit and radial growth of stem in live plants. The sensors are developed using a low-cost and roll-to-roll screen printing technology. A single integrated flexible patch that contains temperature, humidity, salicylic acid, and ethylene sensors, is installed on the leaves of live plants. The strain sensor with in-built pressure correction capability is wrapped around the plant stem to provide pressure-compensated stem diameter measurements. The sensors provide real-time information on plant health under different amounts of water stress conditions. The sensor suite is installed on bell pepper plants for 40 days and measurements of salicylic acid, ethylene, temperature, humidity, and stem diameter are recorded daily. In addition, sensors are installed on different parts of the same plant to investigate the spatiotemporal dynamics of water transport and phytohormone responses. Subsequent correlation and principal component analyses demonstrate the strong association between hormone levels, vapor pressure deficit, and water transport in the plant. Our findings suggest that the mass deployment of PlantFit in agricultural settings will aid growers in detecting water stress/deficiency early and in implementing early intervention measures to reduce stress-induced yield decline.

Deciphering Plant-Insect-Microorganism Signals for Sustainable Crop Production

Agricultural crop productivity relies on the application of chemical pesticides to reduce pest and pathogen damage. However, chemical pesticides also pose a range of ecological, environmental and economic penalties. This includes the development of pesticide resistance by insect pests and pathogens, rendering pesticides less effective. Alternative sustainable crop protection tools should therefore be considered. Semiochemicals are signalling molecules produced by organisms, including plants, microbes, and animals, which cause behavioural or developmental changes in receiving organisms. Manipulating semiochemicals could provide a more sustainable approach to the management of insect pests and pathogens across crops. Here, we review the role of semiochemicals in the interaction between plants, insects and microbes, including examples of how they have been applied to agricultural systems. We highlight future research priorities to be considered for semiochemicals to be credible alternatives to the application of chemical pesticides.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring

Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

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.

Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress

The rising population and the ongoing climate crisis call for improved means to monitor and optimise agriculture. A promising approach to tackle current challenges in food production is the early diagnosis of plant diseases through non-invasive methods, such as the detection of volatiles. However, current devices for detection of multiple volatiles are based on electronic noses, which are expensive, require complex circuit assembly, may involve metal oxides with heating elements, and cannot easily be adapted for some applications that require miniaturisation or limit front-end use of electronic components. To address these challenges, a low-cost optoelectronic nose using chemo-responsive colorimetric dyes drop-casted onto filter paper has been developed in the current work. The final sensors could be used for the quantitative detection of up to six plant volatiles through changes in colour intensities with a sub-ppm level limit of detection, one of the lowest limits of detection reported so far using colorimetric gas sensors. Sensor colouration could be analysed using a low-cost spectrometer and the results could be processed using a microcontroller. The measured volatiles could be used for the early detection of plant abiotic stress as early as two days after exposure to two different stresses: high salinity and starvation. This approach allowed a lowering of costs to GBP 1 per diagnostic sensing paper. Furthermore, the small size of the paper sensors allows for their use in confined settings, such as Petri dishes. This detection of abiotic stress could be easily achieved by exposing the devices to living plants for 1 h. This technology has the potential to be used for monitoring of plant development in field applications, early recognition of stress, implementation of preventative measures, and mitigation of harvest losses.

Optical Sensing Technologies to Elucidate the Interplay between Plant and Microbes

Plant-microbe interactions are critical for ecosystem functioning and driving rhizosphere processes. To fully understand the communication pathways between plants and rhizosphere microbes, it is crucial to measure the numerous processes that occur in the plant and the rhizosphere. The present review first provides an overview of how plants interact with their surrounding microbial communities, and in turn, are affected by them. Next, different optical biosensing technologies that elucidate the plant-microbe interactions and provide pathogenic detection are summarized. Currently, most of the biosensors used for detecting plant parameters or microbial communities in soil are centered around genetically encoded optical and electrochemical biosensors that are often not suitable for field applications. Such sensors require substantial effort and cost to develop and have their limitations. With a particular focus on the detection of root exudates and phytohormones under biotic and abiotic stress conditions, novel low-cost and in-situ biosensors must become available to plant scientists.

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.