Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Chitosan: A versatile polymer for enhancing plant bioactive accumulation, managing plant diseases, and advancing food preservation technologies

Chitosan is a versatile biopolymer composed of N-acetyl D-glucosamine and D-glucosamine units linked by β-(1→4) glycosidic bonds. It is known for its diverse biological applications, which include antimicrobial, antioxidant, antitumor, immunomodulatory, immunoadjuvant, and metal ion chelating abilities. Despite these benefits, the complexity of chitosan's structure limits its use in specific applications, particularly in scalability, solubility, and formulation stability. This review examines chitosan's role in food technology, agriculture, and tissue culture, focusing on its potential to enhance the accumulation of secondary metabolites and its applications in nanotechnology. A comprehensive search of databases, including PubMed, Scopus, and Google Scholar, was conducted to gather relevant literature. Chitosan is used in food technology to preserve seafood and meat, package them, and monitor degradation. Its role in improving crop productivity and plant disease management and promoting growth in both ex-vitro and in-vitro conditions has been discussed, as have chitosan-based nanoformulations as plant growth promoters and biocides. Further research could unlock chitosan's potential to enhance food security, environmental sustainability, and sustainable agriculture. Future research should be directed toward enabling chitosan's broader applications beyond food technology and agriculture. An integrated effort among academic institutions, research centres, and regulatory bodies is needed to bridge the gap between innovation and practical implementation. These efforts include joint research initiatives, policy framework development, capacity building, public-private partnerships, harmonization of standards, and fostering collaboration between industries and regulatory agencies. These efforts aim to validate new technologies, establish shared databases, streamline approval processes, and ensure research outcomes are translatable into regulatory and commercial frameworks.

An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism

This study proposes a model for leafy vegetable disease detection and segmentation based on a few-shot learning framework and a prototype attention mechanism, with the aim of addressing the challenges of complex backgrounds and few-shot problems. Experimental results show that the proposed method performs excellently in both object detection and semantic segmentation tasks. In the object detection task, the model achieves a precision of 0.93, recall of 0.90, accuracy of 0.91, mAP@50 of 0.91, and mAP@75 of 0.90. In the semantic segmentation task, the precision is 0.95, recall is 0.92, accuracy is 0.93, mAP@50 is 0.92, and mAP@75 is 0.92. These results show that the proposed method significantly outperforms the traditional methods, such as YOLOv10 and TinySegformer, validating the advantages of the prototype attention mechanism in enhancing model robustness and fine-grained feature expression. Furthermore, the prototype loss function, which optimizes the distance relationship between samples and category prototypes, significantly improves the model's ability to discriminate between categories. The proposed method shows great potential in agricultural disease detection, particularly in scenarios with few samples and complex backgrounds, offering broad application prospects.

Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m

The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications.

A Systematic Approach for Identifying Unique Genomic Sequences for Fusarium oxysporum f. sp. lactucae Race 1 and Development of Molecular Diagnostic Tools

Fusarium oxysporum f. sp. lactucae (FOLac) is a soil- and seedborne fungal pathogen that causes Fusarium wilt of lettuce, an important disease threatening global lettuce production. Based on pathogenicity on differential lettuce cultivars, four races (1 to 4) have been identified, with race 1 being the only race detected in the United States and the closely related, emerging race 4 known only in Europe. The development of race-specific diagnostic tools is hindered by insufficient genomic data to distinguish between the two races and FOLac from other F. oxysporum formae speciales and nonpathogenic isolates. Here, we describe a systematic approach for developing diagnostic markers for FOLac race 1 that utilized a comprehensive sequence database of F. oxysporum to identify 15 unique genomic sequences. Marker specificity was validated through an exhaustive screening process against genomic data from 797 F. oxysporum isolates representing 64 formae speciales and various plants and non-plant substrates. One of the unique sequences was used to develop a TaqMan quantitative PCR assay and a recombinase polymerase amplification assay, both exhibiting 100% sensitivity and specificity when tested against purified DNA from 171 F. oxysporum isolates and 69 lettuce samples. The relationship between quantitative PCR cycle threshold values and CFU/g values was also determined. This study not only introduces a new marker for FOLac race 1 diagnostics and soil quantitation but also underscores the value of an extensive genomic database and screening software pipeline for developing molecular diagnostics for F. oxysporum formae speciales and other fungal taxa.

TomatoGuard-YOLO: a novel efficient tomato disease detection method

Tomatoes are highly susceptible to numerous diseases that significantly reduce their yield and quality, posing critical challenges to global food security and sustainable agricultural practices. To address the shortcomings of existing detection methods in accuracy, computational efficiency, and scalability, this study propose TomatoGuard-YOLO, an advanced, lightweight, and highly efficient detection framework based on an improved YOLOv10 architecture. The framework introduces two key innovations: the Multi-Path Inverted Residual Unit (MPIRU), which enhances multi-scale feature extraction and fusion, and the Dynamic Focusing Attention Framework (DFAF), which adaptively focuses on disease-relevant regions, substantially improving detection robustness. Additionally, the incorporation of the Focal-EIoU loss function refines bounding box matching accuracy and mitigates class imbalance. Experimental evaluations on a dedicated tomato disease detection dataset demonstrate that TomatoGuard-YOLO achieves an outstanding mAP50 of 94.23%, an inference speed of 129.64 FPS, and an ultra-compact model size of just 2.65 MB. These results establish TomatoGuard-YOLO as a transformative solution for intelligent plant disease management systems, offering unprecedented advancements in detection accuracy, speed, and model efficiency.

Portable solutions for plant pathogen diagnostics: development, usage, and future potential

The increasing prevalence of plant pathogens presents a critical challenge to global food security and agricultural sustainability. While accurate, traditional diagnostic methods are often time-consuming, resource-intensive, and unsuitable for real-time field applications. The emergence of portable diagnostic tools represents a paradigm shift in plant disease management, offering rapid, on-site detection of pathogens with high accuracy and minimal technical expertise. This review explores portable diagnostic technologies' development, deployment, and future potential, including handheld analyzers, smartphone-integrated systems, microfluidics, and lab-on-a-chip platforms. We examine the core technologies underlying these devices, such as biosensors, nucleic acid amplification techniques, and immunoassays, highlighting their applicability to detect bacterial, viral, and fungal pathogens in diverse agricultural settings. Furthermore, the integration of these devices with digital technologies, including the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), is transforming disease surveillance and management. While portable diagnostics have clear advantages in speed, cost-effectiveness, and user accessibility, challenges related to sensitivity, durability, and regulatory standards remain. Innovations in nanotechnology, multiplex detection platforms, and personalized agriculture promise to further enhance the efficacy of portable diagnostics. By providing a comprehensive overview of current technologies and exploring future directions, this review underscores the critical role of portable diagnostics in advancing precision agriculture and mitigating the impact of plant pathogens on global food production.

A Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification

The rapid advancement in smart agriculture has introduced significant challenges, including data scarcity, complex and diverse disease features, and substantial background interference in agricultural scenarios. To address these challenges, a disease detection method based on few-shot learning and diffusion generative models is proposed. By integrating the high-quality feature generation capabilities of diffusion models with the feature extraction advantages of few-shot learning, an end-to-end framework for disease detection has been constructed. The experimental results demonstrate that the proposed method achieves outstanding performance in disease detection tasks. Across comprehensive experiments, the model achieved scores of 0.94, 0.92, 0.93, and 0.92 in precision, recall, accuracy, and mean average precision (mAP@75), respectively, significantly outperforming other comparative models. Furthermore, the incorporation of attention mechanisms effectively enhanced the quality of disease feature representations and improved the model's ability to capture fine-grained features.

A Reusable Capillary Flow-Driven Microfluidic System for Abscisic Acid Detection Using a Competitive Immunoassay

Point-of-care (PoC) devices offer a promising solution for fast, portable, and easy-to-use diagnostics. These characteristics are particularly relevant in agrifood fields like viticulture where the early detection of plant stresses is crucial to crop yield. Microfluidics, with its low reagent volume requirements, is well-suited for such applications. Self-driven microfluidic devices, which rely on capillary forces for fluid motion, offer an attractive alternative by eliminating the need for external pumps and complex fluid control systems. However, traditional microfluidic prototyping materials like polydimethylsiloxane (PDMS) present challenges due to their hydrophobic nature. This paper presents the development of a reusable, portable, capillary-driven microfluidic platform based on a PDMS-PEG (polyethylene glycol) copolymer designed for the rapid low-cost detection of abscisic acid (ABA), a key biomarker for the onset of ripening of non-climacteric fruits and drought stress in vines. By employing passive fluid transport mechanisms, such as capillary-driven sequential flow, this platform enables precise biological and chemical screenings while maintaining portability and ease of use. A simplified field-ready sample processing method is used to prepare the grapes for analysis.

Dual-modal improved biosensing platform for sugarcane smut pathogen based on biological enzyme-Mg2+ DNAzyme coupled with DNA transporter cascading hybridization chain reaction

Sugarcane smut is a major disease affecting the yield and quality of sugarcane, and its early detection is crucial for the healthy development of sugarcane industry. In this work, a dual-modal biosensing platform is designed based on Au-V-MOF and 3D DNA walker for highly sensitive and precise detection of the sugarcane smut pathogen. This detection system utilizes the catalytic properties of biocatalysts and the precise cleavage of DNAzymes, along with 3D DNA walker nanotechnology and a designed "walking track", to achieve amplified detection signals and accurate target recognition. The detection platform utilizes catalytic hairpin assembly for target recycling. Output strand T1 interacts with DNAzyme via a 3D transporter, cleaving S1 in Mg2+ presence. This triggers cascading hybridization chain reaction to form long double-stranded DNA structures that absorb substantial methylene blue (MB). This amplifies the electrical signal and causes a proportional color change due to the redox reaction of MB, which enables electrochemical and colorimetric detection of the target. The method shows a linear response range from 0.0001 to 10,000 pM with a detection limit of 56.76 aM (S/N = 3). It features high sensitivity, specificity, and accuracy, which offers significant potential for early warning and precise detection of sugarcane smut.

A Rapid Field-Visualization Detection Platform for Genetically Modified Soybean 'Zhonghuang 6106' Based on RPA-CRISPR

Genetically modified (GM) herbicide-tolerant soybean 'Zhonghuang 6106', which introduces a glyphosate-resistant gene, ensures soybean yield while allowing farmers to reduce the use of other herbicides, thereby reducing weed management costs. To protect consumer rights and facilitate government supervision, we have established a simple and rapid on-site nucleic acid detection method for GM soybean 'Zhonghuang 6106'. This method leverages the isothermal amplification characteristics of RPA technology and the high specificity of CRISPR-Cas12a to achieve high sensitivity and accuracy in detecting GM soybean components. By optimizing experimental conditions, the platform can quickly produce visual detection results, significantly reducing detection time and improving efficiency. The system can detect down to 10 copies/μL of 'Zhonghuang 6106' DNA templates, and the entire detection process takes about 1 h. The technology also has strong editing capabilities; by redesigning the primers and crRNA in the method, it can become a specific detection method for other GM samples, providing strong technical support for the regulation and safety evaluation of GM crops.

Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images

Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate the impact of various optimizers on classification performance, including AdaDelta, AdaGrad, Adam, RMSprop, Stochastic Gradient Descent (SGD), and Nadam. A diverse dataset comprising 4263 images of eight common tomato pests was used to train and evaluate a customized CNN model. Extensive experiments were conducted to compare the performance of different optimizers in terms of classification accuracy, convergence speed, and robustness. RMSprop achieved the highest validation accuracy of 89.09%, a precision of 88%, recall of 85%, and F1 score of 86% among the optimizers, outperforming other optimizer-based CNN architectures. Additionally, conventional machine learning models such as logistic regression, random forest, naive Bayes classifier, support vector machine, decision tree classifier, and K-nearest neighbors (KNN) were applied to the tomato pest dataset. The best optimizer-based CNN architecture results were compared with these machine learning models. Furthermore, we evaluated the cross-validation results of various optimizers for tomato pest classification. The cross-validation results demonstrate that the Nadam optimizer with CNN outperformed the other optimizer-based approaches and achieved a mean accuracy of 79.12% and F1 score of 78.92%, which is 14.48% higher than the RMSprop optimizer-based approach. The state-of-the-art deep learning models such as LeNet, AlexNet, Xception, Inception, ResNet, and MobileNet were compared with the CNN-optimized approaches and validated the significance of our RMSprop and Nadam-optimized CNN approaches. Our findings provide insights into the effectiveness of each optimizer for tomato pest classification tasks, offering valuable guidance for practitioners and researchers in agricultural image analysis. This research contributes to advancing automated pest detection systems, ultimately aiding in early pest identification and proactive pest management strategies in tomato cultivation.

Advancing agriculture with functional NM: "pathways to sustainable and smart farming technologies"

The integration of nanotechnology in agriculture offers a transformative approach to improving crop yields, resource efficiency, and ecological sustainability. This review highlights the application of functional NM, such as nano-formulated agrochemicals, nanosensors, and slow-release fertilizers, which enhance the effectiveness of fertilizers and pesticides while minimizing environmental impacts. By leveraging the unique properties of NM, agricultural practices can achieve better nutrient absorption, reduced chemical runoff, and improved water conservation. Innovations like nano-priming can enhance seed germination and drought resilience, while nanosensors enable precise monitoring of soil and crop health. Despite the promising commercial potential, significant challenges persist regarding the safety, ecological impact, and regulatory frameworks for nanomaterial use. This review emphasizes the need for comprehensive safety assessments and standardized risk evaluation protocols to ensure the responsible implementation of nanotechnology in agriculture.

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.

Detection Methods for Pine Wilt Disease: A Comprehensive Review

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.

A Comprehensive Review of Climate Change and Plant Diseases in Brazil

Analyzing the impacts of climate change on phytosanitary problems in Brazil is crucial due to the country's special role in global food security as one of the largest producers of essential commodities. This review focuses on the effects of climate change on plant diseases and discusses its main challenges in light of Brazil's diverse agricultural landscape. To assess the risk of diseases caused by fungi, bacteria, viruses, oomycetes, nematodes, and spiroplasms, we surveyed 304 pathosystems across 32 crops of economic importance from 2005 to 2022. Results show that diseases caused by fungi account for 79% of the pathosystems evaluated. Predicting the occurrence of diseases in a changing climate is a complex challenge, and the continuity of this work is strategic for Brazil's agricultural defense. The future risk scenarios analyzed here aim to help guide disease mitigation for cropping systems. Despite substantial progress and ongoing efforts, further research will be needed to effectively prevent economic and environmental damage.

CRISPR-Cas assisted diagnostics of plant viruses and challenges

Plant viruses threaten global food security by infecting commercial crops, highlighting the critical need for efficient virus detection to enable timely preventive measures. Current techniques rely on polymerase chain reaction (PCR) for viral genome amplification and require laboratory conditions. This review explores the applications of CRISPR-Cas assisted diagnostic tools, specifically CRISPR-Cas12a and CRISPR-Cas13a/d systems for plant virus detection and analysis. The CRISPR-Cas12a system can detect viral DNA/RNA amplicons and can be coupled with PCR or isothermal amplification, allowing multiplexed detection in plants with mixed infections. Recent studies have eliminated the need for expensive RNA purification, streamlining the process by providing a visible readout through lateral flow strips. The CRISPR-Cas13a/d system can directly detect viral RNA with minimal preamplification, offering a proportional readout to the viral load. These approaches enable rapid viral diagnostics within 30 min of leaf harvest, making them valuable for onsite field applications. Timely identification of diseases associated with pathogens is crucial for effective treatment; yet developing rapid, specific, sensitive, and cost-effective diagnostic technologies remains challenging. The current gold standard, PCR technology, has drawbacks such as lengthy operational cycles, high costs, and demanding requirements. Here we update the technical advancements of CRISPR-Cas in viral detection, providing insights into future developments, versatile applications, and potential clinical translation. There is a need for approaches enabling field plant viral nucleic acid detection with high sensitivity, specificity, affordability, and portability. Despite challenges, CRISPR-Cas-mediated pathogen diagnostic solutions hold robust capabilities, paving the way for ideal diagnostic tools. Alternative applications in virus research are also explored, acknowledging the technology's limitations and challenges.

A Loop-Mediated Isothermal Amplification (LAMP) Assay for the Detection of Bremia lactucae in the Field

A real-time loop-mediated isothermal amplification (LAMP) assay for the detection of Bremia lactucae, the causal pathogen of lettuce downy mildew, was developed and validated to aid in-field detection of airborne inoculum. Assay specificity was confirmed against a range of other pathogenic oomycete and fungal spp., and sensitivity of the assay for the detection of DNA extracted from sporangia was evaluated. The B. lactucae LAMP assay reliably detected DNA equivalent to 1 spore/reaction (16.7 pg DNA/reaction). Following extraction of DNA from Rotorod air samplers, to which sporangial suspensions were added, the assay reliably detected 25 sporangia/Rotorod. Detection of airborne inoculum of B. lactucae collected through the season from air samplers deployed in-field in plots infected with B. lactucae and in commercial lettuce fields in Scotland over two growing seasons was assessed. The method can be deployed on samples collected from commercial lettuce production to inform disease management strategies and limit the use of unnecessary prophylactic pesticide applications.

LAMP Assay to Detect Elsinoë necatrix, an Important Eucalyptus Shoot and Leaf Pathogen

Eucalyptus scab and shoot malformation caused by Elsinoë necatrix is an emerging disease and a serious threat to the global commercial forestry industry. The disease was first discovered in North Sumatra, Indonesia, and now requires a simple and effective method for early pathogen detection. In this study, a rapid and sensitive loop-mediated isothermal amplification (LAMP) assay was developed for E. necatrix. A unique region in a secondary metabolite gene cluster was used as a target for the assay. To test robustness of the assay, LAMP was verified in 15 strains of E. necatrix. A specificity test against 23 closely related Elsinoë species and three fungal species commonly isolated on Eucalyptus showed that the LAMP assay exclusively identified E. necatrix isolates. The assay had a high level of sensitivity, able to detect 0.01 ng (approximately 400 target copies) of pure E. necatrix DNA. Furthermore, using a simple DNA extraction method, it was possible to use this assay to detect E. necatrix in infected Eucalyptus leaves.

Measuring air metagenomic diversity in an agricultural ecosystem

All species shed DNA during life or in death, providing an opportunity to monitor biodiversity via environmental DNA (eDNA). In recent years, combining eDNA, high-throughput sequencing technologies, bioinformatics, and increasingly complete sequence databases has promised a non-invasive and non-destructive environmental monitoring tool. Modern agricultural systems are often large monocultures and so are highly vulnerable to disease outbreaks. Pest and pathogen monitoring in agricultural ecosystems is key for efficient and early disease prevention, lower pesticide use, and better food security. Although the air is rich in biodiversity, it has the lowest DNA concentration of all environmental media and yet is the route for windborne spread of many damaging crop pathogens. Our work suggests that ecosystems can be monitored efficiently using airborne nucleic acid information. Here, we show that the airborne DNA of microbes can be recovered, shotgun sequenced, and taxonomically classified, including down to the species level. We show that by monitoring a field growing key crops we can identify the presence of agriculturally significant pathogens and quantify their changing abundance over a period of 1.5 months, often correlating with weather variables. We add to the evidence that aerial eDNA can be used as a source for biomonitoring in terrestrial ecosystems, specifically highlighting agriculturally relevant species and how pathogen levels correlate with weather conditions. Our ability to detect dynamically changing levels of species and strains highlights the value of airborne eDNA in agriculture, monitoring biodiversity changes, and tracking taxa of interest.

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

In the context of global population growth expected in the future, enhancing the agri-food yield is crucial. Plant diseases significantly impact crop production and food security. Modern microfluidics offers a compact and convenient approach for detecting these defects. Although this field is still in its infancy and few comprehensive reviews have explored this topic, practical research has great potential. This paper reviews the principles, materials, and applications of microfluidic technology for detecting plant diseases caused by various pathogens. Its performance in realizing the separation, enrichment, and detection of different pathogens is discussed in depth to shed light on its prospects. With its versatile design, microfluidics has been developed for rapid, sensitive, and low-cost monitoring of plant diseases. Incorporating modules for separation, preconcentration, amplification, and detection enables the early detection of trace amounts of pathogens, enhancing crop security. Coupling with imaging systems, smart and digital devices are increasingly being reported as advanced solutions.

Biosensors for Public Health and Environmental Monitoring: The Case for Sustainable Biosensing

Climate change is a profound crisis that affects every aspect of life, including public health. Changes in environmental conditions can promote the spread of pathogens and the development of new mutants and strains. Early detection is essential in managing and controlling this spread and improving overall health outcomes. This perspective article introduces basic biosensing concepts and various biosensors, including electrochemical, optical, mass-based, nano biosensors, and single-molecule biosensors, as important sustainability and public health preventive tools. The discussion also includes how the sustainability of a biosensor is crucial to minimizing environmental impacts and ensuring the long-term availability of vital technologies and resources for healthcare, environmental monitoring, and beyond. One promising avenue for pathogen screening could be the electrical detection of biomolecules at the single-molecule level, and some recent developments based on single-molecule bioelectronics using the Scanning Tunneling Microscopy-assisted break junctions (STM-BJ) technique are shown here. Using this technique, biomolecules can be detected with high sensitivity, eliminating the need for amplification and cell culture steps, thereby enhancing speed and efficiency. Furthermore, the STM-BJ technique demonstrates exceptional specificity, accurately detects single-base mismatches, and exhibits a detection limit essentially at the level of individual biomolecules. Finally, a case is made here for sustainable biosensors, how they can help, the paradigm shift needed to achieve them, and some potential applications.

Occurrence, distribution, and genetic diversity of faba bean viruses in China

With worldwide cultivation, the faba bean (Vicia faba L.) stands as one of the most vital cool-season legume crops, serving as a major component of food security. China leads global faba bean production in terms of both total planting area and yield, with major production hubs in Yunnan, Sichuan, Jiangsu, and Gansu provinces. The faba bean viruses have caused serious yield losses in these production areas, but previous researches have not comprehensively investigated this issue. In this study, we collected 287 faba bean samples over three consecutive years from eight provinces/municipalities of China. We employed small RNA sequencing, RT-PCR, DNA sequencing, and phylogenetic analysis to detect the presence of viruses and examine their incidence, distribution, and genetic diversity. We identified a total of nine distinct viruses: bean yellow mosaic virus (BYMV, Potyvirus), milk vetch dwarf virus (MDV, Nanovirus), vicia cryptic virus (VCV, Alphapartitivirus), bean common mosaic virus (BCMV, Potyvirus), beet western yellows virus (BWYV, Polerovirus), broad bean wilt virus (BBWV, Fabavirus), soybean mosaic virus (SMV, Potyvirus), pea seed-borne mosaic virus (PSbMV, Potyvirus), and cucumber mosaic virus (CMV, Cucumovirus). BYMV was the predominant virus found during our sampling, followed by MDV and VCV. This study marks the first reported detection of BCMV in Chinese faba bean fields. Except for several isolates from Gansu and Yunnan provinces, our sequence analysis revealed that the majority of BYMV isolates contain highly conserved nucleotide sequences of coat protein (CP). Amino acid sequence alignment indicates that there is a conserved NAG motif at the N-terminal region of BYMV CP, which is considered important for aphid transmission. Our findings not only highlight the presence and diversity of pathogenic viruses in Chinese faba bean production, but also provide target pathogens for future antiviral resource screening and a basis for antiviral breeding.

Advancement of metal oxide nanomaterials on agri-food fronts

The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer

Agricultural pests and diseases pose major losses to agricultural productivity, leading to significant economic losses and food safety risks. However, accurately identifying and controlling these pests is still very challenging due to the scarcity of labeling data for agricultural pests and the wide variety of pest species with different morphologies. To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin Transformer for basic feature extraction. Subsequently, we designed the SCF-FPN module to enhance the basic features to extract richer pest features. Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. Finally, to further improve our detection results, we incorporated non-maximum suppression (Soft NMS) and Cascade R-CNN's cascade structure into the optimization process to ensure more accurate and reliable prediction results. In a detection task involving 28 pest species, our algorithm achieves 92.5%, 91.8%, and 93.7% precision in terms of accuracy, recall, and mean average precision (mAP), respectively, which is an improvement of 12.1%, 5.4%, and 7.6% compared to the original baseline model. The results demonstrate that our method can accurately identify and localize farmland pests, which can help improve farmland's ecological environment.

Micro/nanoengineered agricultural by-products for biomedical and environmental applications

Agriculturally derived by-products generated during the growth cycles of living organisms as secondary products have attracted increasing interest due to their wide range of biomedical and environmental applications. These by-products are considered promising candidates because of their unique characteristics including chemical stability, profound biocompatibility and offering a green approach by producing the least impact on the environment. Recently, micro/nanoengineering based techniques play a significant role in upgrading their utility, by controlling their structural integrity and promoting their functions at a micro and nano scale. Specifically, they can be used for biomedical applications such as tissue regeneration, drug delivery, disease diagnosis, as well as environmental applications such as filtration, bioenergy production, and the detection of environmental pollutants. This review highlights the diverse role of micro/nano-engineering techniques when applied on agricultural by-products with intriguing properties and upscaling their wide range of applications across the biomedical and environmental fields. Finally, we outline the future prospects and remarkable potential that these agricultural by-products hold in establishing a new era in the realms of biomedical science and environmental research.

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.

Early Detection of Fusarium Basal Rot Infection in Onions and Shallots Based on VOC Profiles Analysis

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.

Mitigating Global Challenges: Harnessing Green Synthesized Nanomaterials for Sustainable Crop Production Systems

Green nanotechnology, an emerging field, offers economic and social benefits while minimizing environmental impact. Nanoparticles, pivotal in medicine, pharmaceuticals, and agriculture, are now sourced from green plants and microorganisms, overcoming limitations of chemically synthesized ones. In agriculture, these green-made nanoparticles find use in fertilizers, insecticides, pesticides, and fungicides. Nanofertilizers curtail mineral losses, bolster yields, and foster agricultural progress. Their biological production, preferred for environmental friendliness and high purity, is cost-effective and efficient. Biosensors aid early disease detection, ensuring food security and sustainable farming by reducing excessive pesticide use. This eco-friendly approach harnesses natural phytochemicals to boost crop productivity. This review highlights recent strides in green nanotechnology, showcasing how green-synthesized nanomaterials elevate crop quality, combat plant pathogens, and manage diseases and stress. These advancements pave the way for sustainable crop production systems in the future.

Investigating the ecological implications of nanomaterials: Unveiling plants' notable responses to nano-pollution

The rapid advancement of nanotechnology has led to unprecedented innovations; however, it is crucial to analyze its environmental impacts carefully. This review thoroughly examines the complex relationship between plants and nanomaterials, highlighting their significant impact on ecological sustainability and ecosystem well-being. This study investigated the response of plants to nano-pollution stress, revealing the complex regulation of defense-related genes and proteins, and highlighting the sophisticated defense mechanisms in nature. Phytohormones play a crucial role in the complex molecular communication network that regulates plant responses to exposure to nanomaterials. The interaction between plants and nano-pollution influences plants' complex defense strategies. This reveals the interconnectedness of systems of nature. Nevertheless, these findings have implications beyond the plant domain. The incorporation of hyperaccumulator plants into pollution mitigation strategies has the potential to create more environmentally sustainable urban landscapes and improve overall environmental resilience. By utilizing these exceptional plants, we can create a future in which cities serve as centers of both innovation and ecological balance. Further investigation is necessary to explore the long-term presence of nanoparticles in the environment, their ability to induce genetic changes in plants over multiple generations, and their overall impact on ecosystems. In conclusion, this review summarizes significant scientific discoveries with broad implications beyond the confines of laboratories. This highlights the importance of understanding the interactions between plants and nanomaterials within the wider scope of environmental health. By considering these insights, we initiated a path towards the responsible utilization of nanomaterials, environmentally friendly management of pollution, and interdisciplinary exploration. We have the responsibility to balance scientific advancement and environmental preservation to create a sustainable future that combines nature's wisdom with human innovation.

A low-cost, portable, dual-function readout device for amplification-based point-of-need diagnostics

IMPORTANCE

The first critical step in timely disease management is rapid disease identification, which is ideally on-site detection. Of all the technologies available for disease identification, nucleic acid amplification-based diagnostics are often used due to their specificity, sensitivity, adaptability, and speed. However, the modules to interpret amplification results rapidly, reliably, and easily in resource-limited settings at point-of-need (PON) are in high demand. Therefore, we developed a portable, low-cost, and easy-to-perform device that can be used for amplification readout at PON to enable rapid yet reliable disease identification by users with minimal training.

Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment

Due to an increase in interest towards functional and health-related foods, Panax ginseng sprout has been in the spotlight since it contains a significant amount of saponins which have anti-cancer, -stress, and -diabetic effects. To increase the amount of production as well as decrease the cultivation period, sprouted ginseng is being studied to ascertain its optimal cultivation environment in hydroponics. Although there are studies on functional components, there is a lack of research on early disease prediction along with productivity improvement. In this study, the ginseng sprouts were cultivated in four different hydroponic conditions: control treatment, hydrogen-mineral treatment, Bioblock treatment, and highly concentrated nitrogen treatment. Physical properties were measured, and environmental data were acquired using sensors. Using three algorithms (artificial neural networks, support vector machines, random forest) for germination and rottenness classification, and leaf number and length of stem prediction models, we propose a hierarchical machine learning model that predicts the growth outcome of ginseng sprouts after a week. Based on the results, a regression model predicts the number of leaves and stem length during the growth process. The results of the classifier models showed an F1-score of germination classification of about 99% every week. The rottenness classification model showed an increase from an average of 83.5% to 98.9%. Predicted leaf numbers for week 1 showed an average nRMSE value of 0.27, which decreased by about 33% by week 3. The results for predicting stem length showed a higher performance compared to the regression model for predicting leaf number. These results showed that the proposed hierarchical machine learning algorithm can predict germination and rottenness in ginseng sprout using physical properties.

Real-Time and Rapid Detection of Phytopythium vexans Using Loop-Mediated Isothermal Amplification Assay

Phytopythium vexans (de Bary) Abad, de Cock, Bala, Robideau, A. M. Lodhi & Levesque is an important waterborne and soil-inhabiting oomycete pathogen causing root and crown rot of various plants including certain woody ornamentals, fruit, and forest trees. Early and accurate detection of Phytopythium in the nursery production system is critical, as this pathogen is quickly transported to neighboring healthy plants through the irrigation system. Conventional methods for the detection of this pathogen are tedious, frequently inconclusive, and costly. Hence, a specific, sensitive, and rapid molecular diagnostic method is required to overcome the limitations of traditional identification. In the current study, loop-mediated isothermal amplification (LAMP) for DNA amplification was developed for the identification of P. vexans. It was evaluated using real-time and colorimetric assays. Several sets of LAMP primers were designed and screened, but PVLSU2 was found to be specific to P. vexans as it did not amplify other closely related oomycetes, fungi, and bacteria. Moreover, the developed assays were sensitive enough to amplify DNA up to 102 fg per reaction. The real-time LAMP assay was more sensitive than traditional PCR and culture-based methods to detect infected plant samples. In addition, both LAMP assays detected as few as 100 zoospores suspended in 100 ml water. These LAMP assays are anticipated to save time in P. vexans detection by disease diagnostic laboratories and research institutions and enable early preparedness in the event of disease outbreaks.

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.

A Model for the Determination of Potato Tuber Mass by the Measurement of Carbon Dioxide Concentration

The implementation of advanced precision farming systems, which are becoming relevant due to rapid technological development, requires the invention of new approaches to the diagnostics and control of the growing process of cultivated crops. This is especially relevant for potato, as it is one of the most demanded crops in the world. In the present work, an analytic model of the dependence of potato tubers mass on carbon dioxide concentration under cultivation in a closed vegetation system is presented. The model is based on the quantitative description of starch molecule synthesis from carbon dioxide under photosynthesis. In the frame of this work, a comprehensive description of the proposed model is presented, and the verification of this model was conducted on the basis of experimental data from a closed urban vertical farm with automated climate control. The described model can serve as a basis for the non-contact non-invasive real-time measurement of potato tuber mass under growth in closed vegetation systems, such as vertical farms and greenhouses, as well as orbital and space crop production systems.

CRISPR/Cas-Based Diagnostics in Agricultural Applications

Pests and disease-causing pathogens frequently impede agricultural production. An early and efficient diagnostic tool is crucial for effective disease management. Clustered regularly interspaced short palindromic repeats (CRISPR) and the CRISPR-associated protein (Cas) have recently been harnessed to develop diagnostic tools. The CRISPR/Cas system, composed of the Cas endonuclease and guide RNA, enables precise identification and cleavage of the target nucleic acids. The inherent sensitivity, high specificity, and rapid assay time of the CRISPR/Cas system make it an effective alternative for diagnosing plant pathogens and identifying genetically modified crops. Furthermore, its potential for multiplexing and suitability for point-of-care testing at the field level provide advantages over traditional diagnostic systems such as RT-PCR, LAMP, and NGS. In this review, we discuss the recent developments in CRISPR/Cas based diagnostics and their implications in various agricultural applications. We have also emphasized the major challenges with possible solutions and provided insights into future perspectives and potential applications of the CRISPR/Cas system in agriculture.

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.

Advancing the automation of plant nucleic acid extraction for rapid diagnosis of plant diseases in space

Human space exploration missions will continue the development of sustainable plant cultivation in what are obviously novel habitat settings. Effective pathology mitigation strategies are needed to cope with plant disease outbreaks in any space-based plant growth system. However, few technologies currently exist for space-based diagnosis of plant pathogens. Therefore, we developed a method of extracting plant nucleic acid that will facilitate the rapid diagnosis of plant diseases for future spaceflight applications. The microHomogenizer^™ from Claremont BioSolutions, originally designed for bacterial and animal tissue samples, was evaluated for plant-microbial nucleic acid extractions. The microHomogenizer^™ is an appealing device in that it provides automation and containment capabilities that would be required in spaceflight applications. Three different plant pathosystems were used to assess the versatility of the extraction process. Tomato, lettuce, and pepper plants were respectively inoculated with a fungal plant pathogen, an oomycete pathogen, and a plant viral pathogen. The microHomogenizer^™, along with the developed protocols, proved to be an effective mechanism for producing DNA from all three pathosystems, in that PCR and sequencing of the resulting samples demonstrated clear DNA-based diagnoses. Thus, this investigation advances the efforts to automate nucleic acid extraction for future plant disease diagnosis in space.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Bibliometric analysis of real-time PCR-based pathogen detection in plant protection research: a comprehensive study

Introduction

The discovery of RT-PCR-based pathogen detection and gene expression analysis has had a transformative impact on the field of plant protection. This study aims to analyze the global research conducted between 2001 and 2021, focusing on the utilization of RT-PCR techniques for diagnostic assays and gene expression level studies. By retrieving data from the 'Dimensions' database and employing bibliometric visualization software, this analysis provides insights into the major publishing journals, institutions involved, leading journals, influential authors, most cited articles, and common keywords.

Methods

The 'Dimensions' database was utilized to retrieve relevant literature on RT-PCR-based pathogen detection. Fourteen distinct search queries were employed, and the resulting dataset was analyzed for trends in scholarly publications over time. The bibliometric visualization software facilitated the identification of major publishing journals, institutions, leading journals, influential authors, most cited articles, and common keywords. The study's search query was based on the conjunction 'AND', ensuring a comprehensive analysis of the literature.

Results

The analysis revealed a significant increase in the number of scholarly publications on RT-PCR-based pathogen detection over the years, indicating a growing interest and investment in research within the field. This finding emphasizes the importance of ongoing investigation and development, highlighting the potential for further advancements in knowledge and understanding. In terms of publishing journals, Plos One emerged as the leading journal, closely followed by BMC Genomics and Phytopathology. Among the highly cited journals were the European Journal of Plant Pathology, BMC Genomics, and Fungal Genetics and Biology. The publications with the highest number of citations and publications were associated with the United Nations and China. Furthermore, a network visualization map of co-authorship analysis provided intriguing insights into the collaborative nature of the research. Out of 2,636 authors analyzed, 50 surpassed the level threshold, suggesting active collaboration among researchers in the field.

Discussion

Overall, this bibliometric analysis demonstrates that the research on RT-PCR-based pathogen detection is thriving. However, there is a need for further strengthening using modern diagnostic tools and promoting collaboration among well-equipped laboratories. The findings underscore the significance of RT-PCR-based pathogen detection in plant protection and highlight the potential for continued advancements in this field. Continued research and collaboration are vital for enhancing knowledge, developing innovative diagnostic tools, and effectively protecting plants from pathogens.

Plant disease risk is modified by multiple global change drivers

Plant diseases are strongly influenced by host biodiversity, spatial structure, and abiotic conditions. All of these are undergoing rapid change, as the climate is warming, habitats are being lost, and nitrogen deposition is changing nutrient dynamics of ecosystems with ensuing consequences for biodiversity. Here, I review examples of plant-pathogen associations to demonstrate how our ability to understand, model and predict disease dynamics is becoming increasingly difficult, as both plant and pathogen populations and communities are undergoing extensive change. The extent of this change is influenced via both direct and combined effects of global change drivers, and especially the latter are still poorly understood. Change at one trophic level is expected to drive change also at the other, and hence feedback loops between plants and their pathogens are expected to drive changes in disease risk both through ecological as well as evolutionary mechanisms. Many of the examples discussed here demonstrate an increase in disease risk as a result of ongoing change, suggesting that unless we successfully mitigate global environmental change, plant disease is going to become an increasingly heavy burden on our societies with far-reaching consequences for food security and functioning of ecosystems.

A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

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.

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.

Whole genome characterization and diagnostics of prunus necrotic ringspot virus (PNRSV) infecting apricot in India

Prunus necrotic ringspot virus (PNRSV) is a pathogen that infects Prunus species worldwide, causing major economic losses. Using one and two-step RT-PCR and multiplex RT-PCR, the whole genome of the PNRSV-infecting apricot was obtained and described in this study. Computational approaches were used to investigate the participation of several regulatory motifs and domains of the Replicase1, Replicase2, MP, and CP. A single degenerated reverse and three forward oligo primers were used to amplify PNRSV's tripartite genome. The size of RNA1 was 3.332 kb, RNA2 was 2.591 kb, and RNA3 was 1.952 kb, according to the sequencing analysis. The Sequence Demarcation Tool analysis determined a percentage pair-wise identity ranging between 91 and 99% for RNA1 and 2, and 87-98% for RNA3. Interestingly, the phylogenetic analysis revealed that closely related RNA1, RNA2, and RNA3 sequences of PNRSV strains from various geographical regions of the world are classified into distinct clades or groups. This is the first report on the characterization of the whole genome of PNRSV from India, which provides the cornerstone for further studies on the molecular evolution of this virus. This study will assist in molecular diagnostics and management of the diseases caused by PNRSV.

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

Optimization of SAW Sensors for Nanoplastics and Grapevine Virus Detection

In this work, we report the parametric optimization of surface acoustic wave (SAW) delay lines on Lithium niobate for environmental monitoring applications. First, we show that the device performance can be improved by acting opportunely on geometrical design parameters of the interdigital transducers such as the number of finger pairs, the finger overlap length and the distance between the emitter and the receiver. Then, the best-performing configuration is employed to realize SAW sensors. As aerosol particulate matter (PM) is a major threat, we first demonstrate a capability for the detection of polystyrene particles simulating nanoparticulates/nanoplastics, and achieve a limit of detection (LOD) of 0.3 ng, beyond the present state-of-the-art. Next, the SAW sensors were used for the first time to implement diagnostic tools able to detect Grapevine leafroll-associated virus 3 (GLRaV-3), one of the most widespread viruses in wine-growing areas, outperforming electrochemical impedance sensors thanks to a five-times better LOD. These two proofs of concept demonstrate the ability of miniaturized SAW sensors for carrying out on-field monitoring campaigns and their potential to replace the presently used heavy and expensive laboratory instrumentation.

Research on Artificial Intelligence based Fruit Disease Identification System (AI-FDIS) with the Internet of Things (IoT)

Monitoring fruit quality, volume, and development on the plantation are critical to ensuring that the fruits are harvested at the optimal time. Fruits are more susceptible to the disease while they are actively growing. It is possible to safeguard and enhance agricultural productivity by early detection of fruit diseases. A huge farm makes it tough to inspect each tree to learn about its fruit personally. There are several applications for image processing with the Internet of Things (IoT) in various fields. To safeguard the fruit trees from illness and weather conditions, it is difficult for the farmers and their workers to regularly examine these large areas. With the advent of Precision Farming, a new way of thinking about agriculture has emerged, incorporating cutting-edge technological innovations. One of the modern farmers’ biggest challenges is detecting fruit diseases in their early stages. If infections aren’t identified in time, farmers might see a drop in income. Hence this paper is about an Artificial Intelligence Based Fruit Disease Identification System (AI-FDIS) with a drone system featuring a high-accuracy camera, substantial computing capability, and connectivity for precision farming. As a result, it is possible to monitor large agricultural areas precisely, identify diseased plants, and decide on the chemical to spray and the precise dosage to use. It is connected to a cloud server that receives images and generates information from these images, including crop production projections. The farm base can interface with the system with a user-friendly Human-Robot Interface (HRI). It is possible to handle a vast area of farmland daily using this method. The agricultural drone is used to reduce environmental impact and boost crop productivity.

A CRISPR-based lateral flow assay for plant genotyping and pathogen diagnostics

Efficient pathogen diagnostics and genotyping methods enable effective disease management and breeding, improve crop productivity and ensure food security. However, current germplasm selection and pathogen detection techniques are laborious, time-consuming, expensive and not easy to mass-scale application in the field. Here, we optimized a field-deployable lateral flow assay, Bio-SCAN, as a highly sensitive tool to precisely identify elite germplasm and detect mutations, transgenes and phytopathogens in <1 h, starting from sample isolation to result output using lateral flow strips. As a proof of concept, we genotyped various wheat germplasms for the Lr34 and Lr67 alleles conferring broad-spectrum resistance to stripe rust, confirmed the presence of synthetically produced herbicide-resistant alleles in the rice genome and screened for the presence of transgenic elements in the genome of transgenic tobacco and rice plants with 100% specificity. We also successfully applied this new assay to the detection of phytopathogens, including viruses and bacterial pathogens in Nicotiana benthamiana, and two destructive fungal pathogens (Puccinia striiformis f. sp. tritici and Magnaporthe oryzae Triticum) in wheat. Our results illustrate the power of Bio-SCAN in crop breeding, genetic engineering and pathogen diagnostics to enhance food security. The high sensitivity, simplicity, versatility and in-field deployability make the Bio-SCAN as an attractive molecular diagnostic tool for diverse applications in agriculture.