Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 μg/mL), outperforming carbendazim (EC50 = 0.360 μg/mL) and boscalid (EC50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

Surface-Imprinted Polysiloxane with Recognition Ability Based on an ITO Layer for Rapid Detection of Fusarium oxysporum f. sp. cubense by the Naked Eye

Direct observation by the naked eye of fluorescence-stained microbes adsorbed on surface imprinted polymers (SIPs) is highly challenging and limited by speed, accuracy and the semiquantitative nature of the method. In this study, we tested for the presence of spores of Fusarium oxysporum f. sp. cubense race 4 (Foc4), which cause severe banana Fusarium wilt disease and reduces the area of banana plants. This kind of spore can become dormant in soil, which means that the detection of secreted molecules (molecular imprinting) in soil may be inaccurate; detection methods such as polymerase chain reaction (PCR) and Raman spectroscopy are more accurate but time-consuming and inconvenient. Therefore, a semiquantitative and rapid SIP detection method for Foc4 was proposed. Based on the ITO conductive layer, a reusable and naked-eye-detectable Foc4-PDMS SIP film was prepared with a site density of approximately 9000 mm-2. Adsorption experiments showed that when the Foc4 spore concentration was between 104 to 107 CFU/mL, the number of Foc4 spores adsorbed and the fluorescence intensity were strongly correlated with the concentration and could be fully distinguished by the naked eye after fluorescence staining. Adsorption tests on other microbes showed that the SIP film completely recognized only the Foc series. All the results were highly consistent with the naked-eye observations after fluorescence staining, and the results of the Foc4-infected soil experiment were also close to the ideal situation. Taken together, these results showed that Foc4-PDMS SIPs have the ability to rapidly and semiquantitatively detect the concentration of Foc in soil, which can provide good support for banana cultivation. This method also has potential applications in the detection of other fungal diseases.

Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50-60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development.

Evaluation of ALA-capped silver, copper, and silver-copper nanoparticles for controlling fungal plant pathogens

Phytopathogenic fungi significantly threaten global food security, causing substantial yield and quality losses. Sustainable solutions are urgently needed to combat these agricultural pathogens. This study explored the potential of silver (Ag), copper (Cu), and combined Ag/Cu nanoparticles capped with aminolevulinic acid (ALA) as antifungal agents. The nanoparticles (ALAAg, ALACu, and ALAAgCu) were synthesized via photoreduction and characterized using various techniques (UV-Vis, TEM, XRD, Zeta potential). Their antifungal activity against four key plant pathogens (Alternaria grandis, Colletotrichum truncatum, Corynespora cassiicola, and Fusarium oxysporum) was evaluated using poisoned food techniques. Notably, ALAAgCuNPs demonstrated superior antifungal activity compared to a conventional fungicide against two fungal strains. Even at lower concentrations, ALAAgCuNPs exhibited fungistatic effects comparable to those of the control. These promising results suggest the potential of ALAAgCu NPs as a broad-spectrum, potentially eco-friendly alternative for fungal control in plants and seeds. This approach is crucial for ensuring crop health, harvest quality, and food safety.

The Need and Opportunity to Update the Inventory of Plant Pathogenic Fungi and Oomycetes in Mexico

Mexico generates specific phytosanitary regulations for each product and origin to prevent the entry of quarantine pests and/or delay their spread within the national territory, including fungi and oomycetes. Phytosanitary regulations are established based on available information on the presence or absence of these pathogens in the country; however, the compilation and precise analysis of reports is a challenging task due to many publications lacking scientific rigor in determining the presence of a taxon of phytosanitary interest in the country. This review evaluated various studies reporting the presence of plant pathogenic fungi and oomycetes in Mexico and concluded that some lists of diseases and phytopathogenic organisms lack technical-scientific basis. Thus, it highlights the need and presents an excellent opportunity to establish a National Collection of Fungal Cultures and a National Herbarium for obligate parasites, as well as to generate a National Database of Phytopathogenic Fungi and Oomycetes present in Mexico, supported by the combination of morphological, molecular, epidemiological, pathogenicity, symptom, and micrograph data. If realized, this would have a direct impact on many future applications related to various topics, including quarantines, risk analysis, biodiversity studies, and monitoring of fungicide resistance, among others.

Advances in Synthesis and Applications of Single-Atom Catalysts for Metal Oxide-Based Gas Sensors

As a stable, low-cost, environment-friendly, and gas-sensitive material, semiconductor metal oxides have been widely used for gas sensing. In the past few years, single-atom catalysts (SACs) have gained increasing attention in the field of gas sensing with the advantages of maximized atomic utilization and unique electronic and chemical properties and have successfully been applied to enhance the detection sensitivity and selectivity of metal oxide gas sensors. However, the application of SACs in gas sensors is still in its infancy. Herein, we critically review the recent advances and current status of single-atom catalysts in metal oxide gas sensors, providing some suggestions for the development of this field. The synthesis methods and characterization techniques of SAC-modified metal oxides are summarized. The interactions between SACs and metal oxides are crucial for the stable loading of single-atom catalysts and for improving gas-sensitive performance. Then, the current application progress of various SACs (Au, Pt, Cu, Ni, etc.) in metal oxide gas sensors is introduced. Finally, the challenges and perspectives of SACs in metal oxide gas sensors are presented.

Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems

In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method's effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method's capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

Novel 5-Sulfonyl-1,3,4-thiadiazole-Substituted Flavonoids as Potential Bactericides and Fungicides: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship Studies

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae, Rhizoctonia solani, and Colletotrichum orbiculare. Compounds 1, 3, 7, 9, 13, and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC50 values below 10 μg/mL, which were superior to bismerthiazol (70.89 μg/mL). Compound 2 (EC50 = 0.41 μg/mL) displayed the most effective inhibitory potency against R. solani in vivo, comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35-53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani, with an EC50 value of 0.28 μg/mL and a half-maximal effective concentration of 0.46 μg/mL.

Computer vision for plant pathology: A review with examples from cocoa agriculture

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.

Instant Candida albicans Detection Using Ultra-Stable Aptamer Conjugated Gold Nanoparticles

Fungal pathogens such as Candida albicans have significant impacts on women's health and the economy worldwide. Current detection methods often require access to laboratory facilities that are costly, inconvenient, and slow to access. This often leads to self-diagnosis, self-treatment and eventual antifungal resistance. We have created a rapid (within five minutes), cost-effective, and user-friendly method for the early detection of Candida albicans. Our platform utilises aptamer-tagged-gold-core-shell nanoparticles for Candida albicans detection based on the presence of 1,3-β-d glucan molecules. Nanoparticle aggregation occurs in the presence of Candida albicans fungal cells, causing a redshift in the UV-visible absorbance, turning from pink/purple to blue. This colour change is perceptible by the naked eye and provides a "yes"/"no" result. Our platform was also capable of detecting Candida albicans from individual yeast colonies without prior sample processing, dilution or purification. Candida albicans yeast cells were detected with our platform at concentrations as low as 5 × 105 cells within a 50 μL sample volume. We believe that this technology has the potential to revolutionise women's health, enabling women to test for Candida albicans accurately and reliably from home. This approach would be advantageous within remote or developing areas.

AtRAC7/ROP9 Small GTPase Regulates A. thaliana Immune Systems in Response to B. cinerea Infection

Botrytis cinerea is a necrotrophic fungus that can cause gray mold in over 1400 plant species. Once it is detected by Arabidopsis thaliana, several defense responses are activated against this fungus. The proper activation of these defenses determines plant susceptibility or resistance. It has been proposed that the RAC/ROP small GTPases might serve as a molecular link in this process. In this study, we investigate the potential role of the Arabidopsis RAC7 gene during infection with B. cinerea. For that, we evaluated A. thaliana RAC7-OX lines, characterized by the overexpression of the RAC7 gene. Our results reveal that these RAC7-OX lines displayed increased susceptibility to B. cinerea infection, with enhanced fungal colonization and earlier lesion development. Additionally, they exhibited heightened sensitivity to bacterial infections caused by Pseudomonas syringae and Pectobacterium brasiliense. By characterizing plant canonical defense mechanisms and performing transcriptomic profiling, we determined that RAC7-OX lines impaired the plant transcriptomic response before and during B. cinerea infection. Global pathway analysis of differentially expressed genes suggested that RAC7 influences pathogen perception, cell wall homeostasis, signal transduction, and biosynthesis and response to hormones and antimicrobial compounds through actin filament modulation. Herein, we pointed out, for first time, the negative role of RAC7 small GTPase during A. thaliana-B. cinerea interaction.

Nanotechnology-based fungal detection and treatment: current status and future perspective

Fungal infections impose a significant impact on global health and encompass major expenditures in medical treatments. Human mycoses, a fungal co-infection associated with SARS-CoV-2, is caused by opportunistic fungal pathogens and is often overlooked or misdiagnosed. Recently, there is increasing threat about spread of antimicrobial resistance in fungus, mostly in hospitals and other healthcare facilities. The diagnosis and treatment of fungal infections are associated with several issues, including tedious and non-selective detection methods, the growth of drug-resistant bacteria, severe side effects, and ineffective drug delivery. Thus, a rapid and sensitive diagnostic method and a high-efficacy and low-toxicity therapeutic approach are needed. Nanomedicine has emerged as a viable option for overcoming these limitations. Due to the unique physicochemical and optical properties of nanomaterials and newer biosensing techniques, nanodiagnostics play an important role in the accurate and prompt differentiation and detection of fungal diseases. Additionally, nano-based drug delivery techniques can increase drug permeability, reduce adverse effects, and extend systemic circulation time and drug half-life. This review paper is aimed at highlighting recent, promising, and unique trends in nanotechnology to design and develop diagnostics and treatment methods for fungal diseases.

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce (Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Characterization of the Cassava Mycobiome in Symptomatic Leaf Tissues Displaying Cassava Superelongation Disease

Superelongation disease (SED) is a fungal disease that affects cassava in the Caribbean. The symptoms include the appearance of dry necrotic spots and lesions on the leaves, which may severely affect the plant yield. However, the primary causal pathogen is difficult to culture and isolate in the lab because of its slow growth and potential contamination from faster-growing organisms. In addition, the leaf symptoms can be confused with those caused by other pathogens that produce similar necrotic spots and scab-like lesions. There is also little or no information on the contribution of endophytes, if any, to disease symptoms in cassava, a plant where the disease is prevalent. Therefore, this study aimed to characterize the fungal communities in cassava associated with SED symptoms by analyzing gross fungal morphology and performing metagenomics profiling. First, several individual pathogenic fungi were isolated and cultured from diseased cassava leaf tissues from seven locations in Barbados (BB). Both culture isolation and molecular community analyses showed the presence of several other fungi in the disease microenvironment of symptomatic cassava leaves. These included Fusarium, Colletotrichum, and Alternaria species and the suspected species Elsinoë brasiliensis synonym Sphaceloma manihoticola. Additionally, a community analysis using ITS2 amplicon sequencing of 21 symptomatic leaf tissues from BB, St. Vincent and the Grenadines (SVG), Trinidad and Tobago (TT), and Jamaica (JA) revealed that the disease symptoms of superelongation may also result from the interactions of fungal communities in the mycobiome, including Elsinoë species and other fungi such as Colletotrichum, Cercospora, Alternaria, and Fusarium. Therefore, we suggest that examining the pathobiome concept in SED in the future is necessary.

Anticancer and Antiphytopathogenic Activity of Fluorinated Isatins and Their Water-Soluble Hydrazone Derivatives

A series of new fluorinated 1-benzylisatins was synthesized in high yields via a simple one-pot procedure in order to explore the possible effect of ortho-fluoro (3a), chloro (3b), or bis-fluoro (3d) substitution on the biological activity of this pharmacophore. Furthermore, the new isatins could be converted into water-soluble isatin-3-hydrazones using their acid-catalyzed reaction with Girard's reagent P and its dimethyl analog. The cytotoxic action of these substances is associated with the induction of apoptosis caused by mitochondrial membrane dissipation and stimulated reactive oxygen species production in tumor cells. In addition, compounds 3a and 3b exhibit platelet antiaggregation activity at the level of acetylsalicylic acid, and the whole series of fluorine-containing isatins does not adversely affect the hemostasis system as a whole. Among the new water-soluble pyridinium isatin-3-acylhydrazones, compounds 7c and 5c,e exhibit the highest antagonistic effect against phytopathogens of bacterial and fungal origin and can be considered useful leads for combating plant diseases.

Sample-to-answer sensing technologies for nucleic acid preparation and detection in the field

Efficient sample preparation and accurate disease diagnosis under field conditions are of great importance for the early intervention of diseases in humans, animals, and plants. However, in-field preparation of high-quality nucleic acids from various specimens for downstream analyses, such as amplification and sequencing, is challenging. Thus, developing and adapting sample lysis and nucleic acid extraction protocols suitable for portable formats have drawn significant attention. Similarly, various nucleic acid amplification techniques and detection methods have also been explored. Combining these functions in an integrated platform has resulted in emergent sample-to-answer sensing systems that allow effective disease detection and analyses outside a laboratory. Such devices have a vast potential to improve healthcare in resource-limited settings, low-cost and distributed surveillance of diseases in food and agriculture industries, environmental monitoring, and defense against biological warfare and terrorism. This paper reviews recent advances in portable sample preparation technologies and facile detection methods that have been / or could be adopted into novel sample-to-answer devices. In addition, recent developments and challenges of commercial kits and devices targeting on-site diagnosis of various plant diseases are discussed.

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

Scope of Onsite, Portable Prevention Diagnostic Strategies for Alternaria Infections in Medicinal Plants

Medicinal plants are constantly challenged by different biotic inconveniences, which not only cause yield and economic losses but also affect the quality of products derived from them. Among them, Alternaria pathogens are one of the harmful fungal pathogens in medicinal plants across the globe. Therefore, a fast and accurate detection method in the early stage is needed to avoid significant economic losses. Although traditional methods are available to detect Alternaria, they are more time-consuming and costly and need good expertise. Nevertheless, numerous biochemical- and molecular-based techniques are available for the detection of plant diseases, but their efficacy is constrained by differences in their accuracy, specificity, sensitivity, dependability, and speed in addition to being unsuitable for direct on-field studies. Considering the effect of Alternaria on medicinal plants, the development of novel and early detection measures is required to detect causal Alternaria species accurately, sensitively, and rapidly that can be further applied in fields to speed up the advancement process in detection strategies. In this regard, nanotechnology can be employed to develop portable biosensors suitable for early and correct pathogenic disease detection on the field. It also provides an efficient future scope to convert innovative nanoparticle-derived fabricated biomolecules and biosensor approaches in the diagnostics of disease-causing pathogens in important medicinal plants. In this review, we summarize the traditional methods, including immunological and molecular methods, utilized in plant-disease diagnostics. We also brief advanced automobile and efficient sensing technologies for diagnostics. Here we are proposing an idea with a focus on the development of electrochemical and/or colorimetric properties-based nano-biosensors that could be useful in the early detection of Alternaria and other plant pathogens in important medicinal plants. In addition, we discuss challenges faced during the fabrication of biosensors and new capabilities of the technology that provide information regarding disease management strategies.

Synthesis, Structure-Activity Relationship, and Mechanism of a Series of Diarylhydrazide Compounds as Potential Antifungal Agents

A series of simple diarylhydrazide derivatives (45 examples) were well-designed, prepared, and screened for their antifungal activities both in vitro and in vivo. Bioassay results suggested that all designed compounds had significant activity against Alternaria brassicae (EC₅₀ = 0.30-8.35 μg/mL). Among of them, 2c, as the highest activity compound, could effectively inhibit the growth of plant pathogens Pyricularia oryza, Fusarium solani, Alternaria solani, Alternaria brassicae, and Alternaria alternate and was more potent than carbendazim and thiabendazole. 2c showed almost 100% protection at 200 μg/mL in vivo activity against A. solani in tomato. Moreover, 2c did not affect the germination of cowpea seed and the growth of normal human hepatocytes. The preliminary mechanistic exploration documented that 2c could result in the abnormal morphology and irregular structure of the cell membrane, destroy the function of mitochondria, increase the reactive oxygen species, and inhibit the proliferation of hypha cell. The above results manifested that target compound 2c could be a potential fungicidal candidate against phytopathogenic diseases for its excellent fungicidal activities.

Triadimenol promotes the production of reactive oxygen species and apoptosis with cardiotoxicity and developmental abnormalities in zebrafish

Various types of fungicides, especially triazole fungicides, are used to prevent fungal diseases on farmlands. However, the developmental toxicity of one of the triazole fungicides, triadimenol, remains unclear. Therefore, we used the zebrafish animal model, a representative toxicological model, to investigate it. Triadimenol induced morphological alterations in the eyes and body length along with yolk sac and heart edema. It also stimulated the production of reactive oxygen species and expression of inflammation-related genes and caused apoptosis in the anterior regions of zebrafish, especially in the heart. The phosphorylation levels of Akt, ERK, JNK, and p38 proteins involved in the PI3K and MAPK pathways, which are important for the development process, were also reduced by triadimenol. These changes led to malformation of the heart and vascular structures, as observed in the flk1:eGFP transgenic zebrafish models and a reduction in the heart rate. In addition, the expression of genes associated with cardiac and vascular development was also reduced. Therefore, we elucidated the mechanisms associated with triadimenol toxicity that leads to various abnormalities and developmental toxicity in zebrafish.

The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm (Multi-head self-attention and Ghost-optimized YOLO) is proposed to detect gray mold spores rapidly. Firstly, Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores. Secondly, we combine weighted Bidirectional Feature Pyramid Network (BiFPN) to fuse multiscale features of different layers. Then, a lightweight network is used to construct GhostCSP to optimize the neck part. Cucumber gray mold spores are used as the study object. The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image, which is significantly better than the state-of-the-art model. The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred, small targets, multimorphology, and high-density scenes. Meanwhile, compared with the YOLOv5 model, the detection accuracy of the improved model is improved by 6.8%. It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

In the construction of electronic nose devices, two groups of measurement setups could be distinguished when we take into account the design of electronic nose chambers. The simpler one consists of placing the sensors directly in the environment of the measured gas, which has an important advantage, in that the composition of the gas is not changed as the gas is not diluted. However, that has an important drawback in that it is difficult to clean sensors between measurement cycles. The second, more advanced construction, contains a pneumatic system transporting the gas inside a specially designed sensor chamber. A new design of an electronic nose gas sensor chamber is proposed, which consists of a sensor chamber with a sliding chamber shutter, equipped with a simple pneumatic system for cleaning the air. The proposal combines the advantages of both approaches to the sensor chamber designs. The sensors can be effectively cleared by the flow of clean air, while the measurements are performed in the open state when the sensors are directly exposed to the measured gas. Airflow simulations were performed to confirm the efficiency of clean air transport used for sensors' cleaning. The demonstrated electronic nose applies eight Figaro Co. MOS TGS series sensors, in which a transient response caused by a change of the exposition to measured gas, and change of heater voltage, was collected. The new electronic nose was tested as applied to the differentiation between the samples of Ciboria batschiana fungi, which is one of the most harmful pathogens of stored acorns. The samples with various coverage, thus various concentrations of the studied odor, were measured. The tested device demonstrated low noise and a good level of repetition of the measurements, with stable results during several hours of repetitive measurements during an experiment lasting five consecutive days. The obtained data allowed complete differentiation between healthy and infected samples.

Design, Synthesis and Bioassay of 2-Phenylglycine Derivatives as Potential Pesticide Candidates

Plant diseases can seriously affect the growth of food crops and economic crops. To date, pesticides are still among the most effective methods to prevent and control plant diseases worldwide. Consequently, to develop potential pesticide molecules, a series of novel 2-phenylglycine derivatives containing 1,3,4-oxadiazole-2-thioethers were designed and synthesized. The bioassay results revealed that G₁₉ exhibited great in vitro antifungal activity against Thanatephorus cucumeris with an EC₅₀ value of 32.4 μg/mL, and in vivo antifungal activity against T. cucumeris on rice leaves at a concentration of 200.0 μg/mL (66.9 %) which was close that of azoxystrobin (73.2 %). Compounds G₂₄ (80.2 %), G₂₅ (89.4 %), and G₂₇ (83.3 %) exhibited impressive in vivo inactivation activity against tobacco mosaic virus (TMV) at a concentration of 500.0 μg/mL, which was comparable to that of ningnanmycin (96.3 %) and markedly higher than that of ribavirin (55.6 %). The antibacterial activity of G₁₆ (63.1 %), G₂₆ (89.9 %), G₂₇ (78.0 %), and G₂₈ (68.0 %) against Xoo at a concentration of 50.0 μg/mL was higher than that of thiadiazole copper (18.0 %) and bismerthiazol (38.9 %). Preliminary mechanism studies on the antifungal activity against T. cucumeris demonstrated that G₁₉ can affect the growth of mycelia by disrupting the integrity of the cell membrane and altering the permeability of the cell. These studies revealed that the amino acid derivatives containing a 1,3,4-oxadiazole moiety exhibited certain antifungal, antibacterial, and anti-TMV activities, and these derivatives can be further modified and developed as potential pesticide molecules.

Nucleic acid amplification-based strategy to detect foodborne pathogens in milk: a review

Milk contaminated with trace amounts of foodborne pathogens can considerably threaten food safety and public health. Therefore, rapid and accurate detection techniques for foodborne pathogens in milk are essential. Nucleic acid amplification (NAA)-based strategies are widely used to detect foodborne pathogens in milk. This review article covers the mechanisms of the NAA-based detection of foodborne pathogens in milk, including polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), rolling circle amplification (RCA), and enzyme-free amplification, among others. Key factors affecting detection efficiency and the advantages and disadvantages of the above techniques are analyzed. Potential on-site detection tools based on NAA are outlined. We found that NAA-based strategies were effective in detecting foodborne pathogens in milk. Among them, PCR was the most reliable. LAMP showed high specificity, whereas RPA and RCA were most suitable for on-site and in-situ detection, respectively, and enzyme-free amplification was more economical. However, factors such as sample separation, nucleic acid target conversion, and signal transduction affected efficiency of NAA-based strategies. The lack of simple and effective sample separation methods to reduce the effect of milk matrices on detection efficiency was noteworthy. Further research should focus on simplifying, integrating, and miniaturizing microfluidic on-site detection platforms.

A Detection Method for Crop Fungal Spores Based on Microfluidic Separation Enrichment and AC Impedance Characteristics

The timely monitoring of airborne crop fungal spores is important for maintaining food security. In this study, a method based on microfluidic separation and enrichment and AC impedance characteristics was proposed to detect spores of fungal pathogens that cause diseases on crops. Firstly, a microfluidic chip with tertiary structure was designed for the direct separation and enrichment of Ustilaginoidea virens spores, Magnaporthe grisea spores, and Aspergillus niger spores from the air. Then, the impedance characteristics of fungal spores were measured by impedance analyzer in the enrichment area of a microfluidic chip. The impedance characteristics of fungal spores were analyzed, and four impedance characteristics were extracted: absolute value of impedance (abs), real part of impedance (real), imaginary part of impedance (imag), and impedance phase (phase). Finally, based on the impedance characteristics of extracted fungal spores, K-proximity (KNN), random forest (RF), and support vector machine (SVM) classification models were established to classify the three fungal spores. The results showed that the microfluidic chip designed in this study could well collect the spores of three fungal diseases, and the collection rate was up to 97. The average accuracy of KNN model, RF model, and SVM model for the detection of three disease spores was 93.33, 96.44 and 97.78, respectively. The F1-Score of KNN model, RF model, and SVM model was 90, 94.65, and 96.18, respectively. The accuracy, precision, recall, and F1-Score of the SVM model were all the highest, at 97.78, 96.67, 96.69, and 96.18, respectively. Therefore, the detection method of crop fungal spores based on microfluidic separation, enrichment, and impedance characteristics proposed in this study can be used for the detection of airborne crop fungal spores, providing a basis for the subsequent detection of crop fungal spores.

2D MXene/graphene nanocomposite preparation and its electrochemical performance towards the identification of nicotine level in human saliva

The quantitative analysis of neurological drugs is critical since the kinetics of body fluids is strongly dependent on the dosage of the drug levels. Thus, the study of neurological medicines is significant because of the major diseases connected to it, for instance, Alzheimer's and Parkinson's diseases. Herein, a 2D hybrid MXene/graphene (MX/Gr) film was synthesized through a top-down approach and utilized to prepare an electrochemical transducer for the electrochemical sensing of nicotine. The X-ray diffraction (XRD), Raman and X-ray photoelectron spectroscopy (XPS) confirmed the successful incorporation of MX with Gr sheets. The high-resolution scanning electron microscopy (HR-SEM) and transmission electron microscopy (TEM) have been used to confirm the formation of MX, graphene sheets and the MX/Gr hybrid film. Furthermore, the MX/Gr hybrid film composite modified glassy carbon electrode (GCE) was prepared to selectively detect the nicotine in phosphate buffer medium (0.1 M PBS, pH~7.4). Under the optimized condition, the MX/Gr/GCE based sensor provided a linear response against nicotine from 1 to 55 µM and 30 nM - 600 nM with the lowest limit of detections (LOD) of 290 nM and 0.28 nM by differential pulse voltammetry (DPV) and amperometry, respectively. This newly developed MX/Gr hybrid film modified electrode displayed a remarkable selectivity, sensitivity, and reproducibility for accurate detection of nicotine. Finally, this new sensor was applied to detect nicotine in human/artificial saliva samples with high accuracy.

Novel Coumarin 7-Carboxamide/Sulfonamide Derivatives as Potential Fungicidal Agents: Design, Synthesis, and Biological Evaluation

Coumarin compounds have a variety of biological activities such as anti-tumor, anti-coagulation, anti-HIV, anti-fungal, and insecticidal. Amide and sulfonamide compounds have been used as fungicides for half a century, and dozens of varieties have been developed so far. This study focused on the introduction of carboxamide and sulfonamide moieties in a coumarin core to discover novel derivatives. Based on this strategy, we synthesized two series of novel carboxamide and sulfonamide substituted coumarin derivatives, and their fungicidal activity was also investigated. Some designed compounds possessed potential activities against six phytopathogenic fungi in the primary assays, highlighted by compound 6r. Compound 6r exhibited stronger fungicidal activity against Botrytis cinerea (EC₅₀ = 20.52 µg/mL) and will be the lead structure for further study.

Biosensors for rapid detection of bacterial pathogens in water, food and environment

Conventional techniques (e.g., culture-based method) for bacterial detection typically require a central laboratory and well-trained technicians, which may take several hours or days. However, recent developments within various disciplines of science and engineering have led to a major paradigm shift in how microorganisms can be detected. The analytical sensors which are widely used for medical applications in the literature are being extended for rapid and on-site monitoring of the bacterial pathogens in food, water and the environment. Especially, within the low-resource settings such as low and middle-income countries, due to the advantages of low cost, rapidness and potential for field-testing, their use is indispensable for sustainable development of the regions. Within this context, this paper discusses analytical methods and biosensors which can be used to ensure food safety, water quality and environmental monitoring. In brief, most of our discussion is focused on various rapid sensors including biosensors and microfluidic chips. The analytical performances such as the sensitivity, specificity and usability of these sensors, as well as a brief comparison with the conventional techniques for bacteria detection, form the core part of the discussion. Furthermore, we provide a holistic viewpoint on how future research should focus on exploring the synergy of different sensing technologies by developing an integrated multiplexed, sensitive and accurate sensors that will enable rapid detection for food safety, water and environmental monitoring.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Whole-genome single nucleotide polymorphism analysis for typing the pandemic pathogen Fusarium graminearum sensu stricto

Recent improvements in microbiology and molecular epidemiology were largely stimulated by whole- genome sequencing (WGS), which provides an unprecedented resolution in discriminating highly related genetic backgrounds. WGS is becoming the method of choice in epidemiology of fungal diseases, but its application is still in a pioneer stage, mainly due to the limited number of available genomes. Fungal pathogens often belong to complexes composed of numerous cryptic species. Detecting cryptic diversity is fundamental to understand the dynamics and the evolutionary relationships underlying disease outbreaks. In this study, we explore the value of whole-genome SNP analyses in identification of the pandemic pathogen Fusarium graminearum sensu stricto (F.g.). This species is responsible for cereal diseases and negatively impacts grain production worldwide. The fungus belongs to the monophyletic fungal complex referred to as F. graminearum species complex including at least sixteen cryptic species, a few among them may be involved in cereal diseases in certain agricultural areas. We analyzed WGS data from a collection of 99 F.g. strains and 33 strains representing all known cryptic species belonging to the FGSC complex. As a first step, we performed a phylogenomic analysis to reveal species-specific clustering. A RAxML maximum likelihood tree grouped all analyzed strains of F.g. into a single clade, supporting the clustering-based identification approach. Although, phylogenetic reconstructions are essential in detecting cryptic species, a phylogenomic tree does not fulfill the criteria for rapid and cost-effective approach for identification of fungi, due to the time-consuming nature of the analysis. As an alternative, analysis of WGS information by mapping sequence data from individual strains against reference genomes may provide useful markers for the rapid identification of fungi. We provide a robust framework for typing F.g. through the web-based PhaME workflow available at EDGE bioinformatics. The method was validated through multiple comparisons of assembly genomes to F.g. reference strain PH-1. We showed that the difference between intra- and interspecies variability was at least two times higher than intraspecific variation facilitating successful typing of F.g. This is the first study which employs WGS data for typing plant pathogenic fusaria.

Environmentally Friendly Wheat Farming: Biological and Economic Efficiency of Three Treatments to Control Fungal Diseases in Winter Wheat (Triticum aestivum L.) under Field Conditions

The control of wheat diseases using bioagents is not well studied under field conditions. The present study was aimed at investigating, during four consecutive growing seasons (2017-2020), the efficacy of two integrated crop protection (ICP) systems to control the common wheat diseases for enhancing the productivity and profitability of winter wheat crops and ensuring nutritional and food security. Two environmental-friendly treatments were tested, biological (T1), which contained bioagents and fertilizers, and combined (T2), which included fertilizers and bioagents coupled with lower doses of fungicides. The chemical treatment (T3) was used for comparison with (T1) and (T2). Furthermore, two Russian winter wheat varieties (Nemchinovskaya 17 (V1) and Moscovskaya 40 (V2)) were studied. A randomized complete block design was used with four replicates. Diseases infestation rates for snow mold (SM), root rot (RR), powdery mildew (PM), and Fusarium (Fus), yield performances, and grain quality (measured through protein content) were determined according to the tested treatments, and the economic efficiency was calculated for each treatment. The combined treatment (T2) was the most effective against fungal diseases with 1.8% (SM), 1.2% (RR), 0.9% (PM), and 0.9% (Fus). The highest grain yield (6.8 t·ha-1), protein content (15.2%), and 1000-grain weight (43.7%) were observed for winter wheat variety Moscovskaya 40 with the combined treatment (T2). The highest number of productive stems (N.P.S) (556 stems/m2) was attained for combined treatment (T2), followed by biological treatment (T1) (552 stems/m2) with the variety Nemchinovskaya 17. The profitability (cost-benefit ratio) of the combined treatment (T2) was 2.38 with the Moscovskaya 40 variety (V2), while 2.03 was recorded for the biological treatment. Applying environmentally friendly combined and biological treatments resulted in high wheat yield and net income, as well as healthy products.

Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.

New Detection Method for Fungal Infection in Silver Fir Seeds

Silver fir trees have cycles of low and high seed production, and thus it is necessary to collect seeds in high production years to save them for low production years to ensure the continuity of nursery production. Tree seeds can be stored loosely in piles or containers, but they need to be checked for viability before planting. The objective of this study was to find a quick and inexpensive method to determine the suitability of seed lots for planting. The working hypothesis was that an electronic nose device could be used to detect odors from fungi or from decomposing organic material, and thus aid in determination of whether seeds could be sown or discarded. To affirm and supplement results from the electronic nose, we used gas chromatography–mass spectrometry (GC-MS) to detect volatile secondary metabolites such as limonene and cadienes, which were found at the highest concentrations in both, infected and uninfected seeds. Uninfected seeds contained exceptionally high concentrations of pinene, which are known to be involved in plant resistance responses. Statistically higher levels of terpineol were found in infected seeds than in uninfected seeds. A prototype of our electronic nose partially discriminated between healthy and spoiled seeds, and between green and white fungal colonies grown on incubated seeds. These preliminary observations were encouraging and we plan to develop a practical device that will be useful for forestry and horticulture.

Loop-Mediated Isothermal Amplification for Detection of Plant Pathogens in Wheat (Triticum aestivum)

Wheat plants can be infected by a variety of pathogen species, with some of them causing similar symptoms. For example, Zymoseptoria tritici and Parastagonospora nodorum often occur together and form the Septoria leaf blotch complex. Accurate detection of wheat pathogens is essential in applying the most appropriate disease management strategy. Loop-mediated isothermal amplification (LAMP) is a recent molecular technique that was rapidly adopted for detection of plant pathogens and can be implemented easily for detection in field conditions. The specificity, sensitivity, and facility to conduct the reaction at a constant temperature are the main advantages of LAMP over immunological and alternative nucleic acid-based methods. In plant pathogen detection studies, LAMP was able to differentiate related fungal species and non-target strains of virulent species with lower detection limits than those obtained with PCR. In this review, we explain the amplification process and elements of the LAMP reaction, and the variety of techniques for visualization of the amplified products, along with their advantages and disadvantages compared with alternative isothermal approaches. Then, a compilation of analyses that show the application of LAMP for detection of fungal pathogens and viruses in wheat is presented. We also describe the modifications included in real-time and multiplex LAMP that reduce common errors from post-amplification detection in traditional LAMP assays and allow discrimination of targets in multi-sample analyses. Finally, we discuss the utility of LAMP for detection of pathogens in wheat, its limitations, and current challenges of this technique. We provide prospects for application of real-time LAMP and multiplex LAMP in the field, using portable devices that measure fluorescence and turbidity, or facilitate colorimetric detection. New technologies for detection of plant pathogen are discussed that can be integrated with LAMP to obtain elevated analytical sensitivity of detection.

Recent advances in E-monitoring of plant diseases

Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.

Determination of Specific Parameters for Early Detection of Botrytis cinerea in Lettuce

In horticulture, the demand for efficient farming processes and food industries increases rapidly. Plant diseases cause severe crop production and economic losses. Therefore, early detection and identification of the diseases in plants are critical. This study aimed to determine the specific parameters for early detection of Botrytis cinerea in lettuce. The lettuce “Little Gem” was inoculated with B. cinerea isolate spore suspension and disc to evaluate the plant response to inner and outer infection, respectively. The non-destructive measurements of leaf spectral reflectance indices and biochemical compounds (phenols, proteins, DPPH, FRAP, chlorophyll, and carotenoids) were used to evaluate the plant physiological response to inoculation with B. cinerea after 12, 18, 36, 60, and 84 h. Our data showed that lettuce responded differently to inner and outer inoculation with B. cinerea. Therefore, the findings of this study allow for the inoculation method to be chosen to determine the early plant response to infection with B. cinerea according to specific leaf spectral reflectance indexes and phytochemicals in further research.

Biosensors for Fungal Detection

Due to the serious threat of invasive fungal infections, there is an emergent need for improved a sensitive and more accurate diagnostic tests for detection of systemic pathogenic fungi and plant health. Traditional fungal diagnosis can only be achieved at later growing phases. The complex and difficult immunodiagnostic is also widely employed. Enzyme-based immunoassays which lead to cross-interaction with different fungi still also obeyed. A polymerase chain reactions (PCRs)- based molecular diagnosis are does not enable precise identification of fungal pathogens, or the ability to test isolates for drug sensitivity. In the future, biosensing technologies and nanotechnological tools, will improve diagnosis of pathogenic fungi through a specific and sensitive pathogen detection. This report systematically reviews the most prominent biosensor trends for fungi detection.

Comprehensive account of present techniques for in-field plant disease diagnosis

The early detection of plant pathogens is an appropriate preventive strategy for the management of crop yield and quality. For this reason, effective diagnostic techniques and tools, which are simple, specific, rapid and economic, are needed to be developed. Although several such technologies have been developed still most of them suffer one or the other limitation. Major limitations of the widely used diagnostic methods are requirement of trained staff and laboratory setup. Development of point-of-care diagnostic devices (handy portable devices) that require no specialized staff and can directly be used in fields is need of the hour. The aim of this review is to compile the information on current promising techniques that are in use for plant-pathogen diagnosis. Additionally, it focuses on the latest in-field pathogen diagnostic techniques with associated advantages and limitations.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Electronic Nose Differentiation between Quercus robur Acorns Infected by Pathogenic Oomycetes Phytophthora plurivora and Pythium intermedium

Identification of the presence of pathogenic oomycetes in infected plant material proved possible using an electronic nose, giving hope for a tool to assist nurseries and quarantine services. Previously, species of Phytophthora plurivora and Pythium intermedium have been successfully distinguished in germinated acorns of English oak Quercus robur L. Chemical compound analyses performed by HS-SPME/GC-MS (Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry) revealed the presence of volatile antifungal molecules produced by oak seedlings belonging to terpenes and alkanes. Compounds characteristic only of Phytophthora plurivora or Pythium intermedium were also found. Methylcarveol occurred when germinated acorns were infected with Pythium, while neophytadiene (isomer 2 and 3) occurred only when infected with Phytophthora. Moreover, isopentanol was found in acorns infected with Phytophthora, while in control, isopentyl vinyl ether was not observed anywhere else. Among the numerous volatile compounds, isopentanol only occurred in acorns infected with Phytophthora and methylcarveol in acorns infected with Pythium.

Novel Pyrazole-4-acetohydrazide Derivatives Potentially Targeting Fungal Succinate Dehydrogenase: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship, and Molecular Docking

Succinate dehydrogenase inhibitors (SDHIs) have emerged in fungicide markets as one of the fastest-growing categories that are widely applied in agricultural production for crop protection. Currently, the structural modification focusing on the flexible amide link of SDHI molecules is being gradually identified as one of the innovative strategies for developing novel highly efficient and broad-spectrum fungicides. Based on the above structural features, a series of pyrazole-4-acetohydrazide derivatives potentially targeting fungal SDH were constructed and evaluated for their antifungal effects against Rhizoctonia solani, Fusarium graminearum, and Botrytis cinerea. Strikingly, the in vitro EC₅₀ values of constructed pyrazole-4-acetohydrazides 6w against R. solani, 6c against F. graminearum, and 6f against B. cinerea were, respectively, determined as 0.27, 1.94, and 1.93 μg/mL, which were obviously superior to that of boscalid against R. solani (0.94 μg/mL), fluopyram against F. graminearum (9.37 μg/mL), and B. cinerea (1.94 μg/mL). Concurrently, the effects of the substituent steric, electrostatic, hydrophobic, and hydrogen-bond fields on structure-activity relationships were elaborated by the reliable comparative molecular field analysis and comparative molecular similarity index analysis models. Subsequently, the practical value of pyrazole-4-acetohydrazide derivative 6w as a potential SDHI was ascertained by the relative surveys on the in vivo anti-R. solani preventative efficacy, inhibitory effects against fungal SDH, and molecular docking studies. The present results provide an indispensable complement for the structural optimization of antifungal leads potentially targeting SDH.

Expedient discovery for novel antifungal leads: 1,3,4-Oxadiazole derivatives bearing a quinazolin-4(3H)-one fragment

Developing novel fungicide candidates are intensively promoted by the rapid emergences of resistant fungi that outbreak on agricultural production. Aiming to discovery novel antifungal leads, a series of 1,3,4-oxadiazole derivatives bearing a quinazolin-4(3H)-one fragment were constructed for evaluating their inhibition effects against phytopathogenic fungi in vitro and in vivo. Systematically structural optimizations generated the bioactive molecule I₃₂ that was identified as a promising inhibitor against Rhizoctonia solani with the in vivo preventative effect of 58.63% at 200 μg/mL. The observations that were captured by scanning electron microscopy and transmission electron microscopy demonstrated that the bioactive molecule I₃₂ could induce the sprawling growth of hyphae, the local shrinkage and rupture on hyphal surfaces, the extreme swelling of vacuoles, the striking distortions on cell walls, and the reduction of mitochondria numbers. The above results provided an indispensable complement for the discovery of antifungal lead bearing a quinazolin-4(3H)-one and 1,3,4-oxadiazole fragment.