Plant Diseases Research Articles

A novel type of quantum-delivery system (salicylic acid conjugation based on ZnO quantum dots) alleviates the bacterial fruit blotch disease of melon plants by activating defense response

The use of nano-pesticide delivery systems to suppress crop disease has great potential in sustainable agriculture. Herein, a pH-responsive salicylic acid (SA) conjugation based on aminopropyl-functionalized ZnO quantum dots (SA-ZnO QDs) with a loading capacity of 13.4 wt% was prepared by an amidation reaction. Results indicated that SA-ZnO QDs could release SA rapidly in an acidic condition, which corresponded to the pH of the water-soaked lesions where the bacterial fruit blotch disease often spread. The antibacterial activity of the synthesized SA-ZnO QDs was about 1.6 times and 1.3 times higher than that of SA and aminopropyl-functionalized ZnO QDs, respectively. A pot experiment showed that foliar application of SA-ZnO QDs could reduce the incidence of bacterial fruit blotch disease (54.46%) by increasing the antioxidant defense systems (44.33–79.59%) and nutrient absorption (23.19–26.95%). More importantly, we observed that, after the application of SA-ZnO QDs, the systemic acquired resistance and flavone biosynthesis were activated in infected melon seedlings, which was revealed by metabolomics. This work demonstrates the important role of the quantum-delivery system in improving crop disease suppression for sustainable agriculture.

Arabidopsis WRKY55 Transcription Factor Enhances Soft Rot Disease Resistance with ORA59.

Pectobacterium is a major bacterial causal agent leading to soft rot disease in host plants. With the Arabidopsis-Pectobacterium pathosystem, we investigated the function of an Arabidopsis thaliana WRKY55 during defense responses to Pectobacterium carotovorum ssp. carotovorum (Pcc). Pcc-infection specifically induced WRKY55 gene expression. The overexpression of WRKY55 was resistant to the Pcc infection, while wrky55 knockout plants compromised the defense responses against Pcc. WRKY55 expression was mediated via Arabidopsis COI1-dependent signaling pathway showing that WRKY55 can contribute to the gene expression of jasmonic acid-mediated defense marker genes such as PDF1.2 and LOX2. WRKY55 physically interacts with Arabidopsis ORA59 facilitating the expression of PDF1.2</i. Our results suggest that WRKY55 can function as a positive regulator for resistance against Pcc in Arabidopsis.

Cestrum tomentosum L.f. Extracts against Colletotrichum scovillei by Altering Cell Membrane Permeability and Inducing ROS Accumulation.

Chili pepper anthracnose, caused by Colletotrichum spp., is a significant biotic stress affecting chili fruits globally. While fungicide application is commonly used for disease management due to its efficiency and costeffectiveness, excessive use poses risks to human health and the environment. Botanical fungicides offer advantages such as rapid degradation and low toxicity to mammals, making them increasingly popular for sustainable plant disease control. This study investigated the antifungal properties of Cestrum tomentosum L.f. crude extracts (CTCE) against Colletotrichum scovillei. The results demonstrated that CTCE effectively inhibited conidia germination and germ tube elongation at 40 µg/ml concentrations. Moreover, CTCE exhibited strong antifungal activity against C. scovillei mycelial growth, with an EC50 value of 18.81 µg/ml. In vivo experiments confirmed the protective and curative effects of CTCE on chili pepper fruits infected with C. scovillei. XTT analysis showed that the CTCE could significantly inhibit the cell viability of C. scovillei. Mechanistic studies revealed that CTCE disrupted the plasma membrane integrity of C. scovillei and induced the accumulation of reactive oxygen species in hyphal cells. These findings highlight CTCE as a promising eco-friendly botanical fungicide for managing C. scovillei infections in chili peppers.

The perception and evolution of flagellin, cold shock protein and elongation factor Tu from vector-borne bacterial plant pathogens.

Vector-borne bacterial pathogens cause devastating plant diseases that cost billions of dollars in crop losses worldwide. These pathogens have evolved to be host- and vector-dependent, resulting in a reduced genome size compared to their free-living relatives. All known vector-borne bacterial plant pathogens belong to four different genera: 'Candidatus Liberibacter', 'Candidatus Phytoplasma', Spiroplasma and Xylella. To protect themselves against pathogens, plants have evolved pattern recognition receptors that can detect conserved pathogen features as non-self and mount an immune response. To gain an understanding of how vector-borne pathogen features are perceived in plants, we investigated three proteinaceous features derived from cold shock protein (csp22), flagellin (flg22) and elongation factor Tu (elf18) from vector-borne bacterial pathogens as well as their closest free-living relatives. In general, vector-borne pathogens have fewer copies of genes encoding flagellin and cold shock protein compared to their closest free-living relatives. Furthermore, epitopes from vector-borne pathogens were less likely to be immunogenic compared to their free-living counterparts. Most Liberibacter csp22 and elf18 epitopes do not trigger plant immune responses in tomato or Arabidopsis. Interestingly, csp22 from the citrus pathogen 'Candidatus Liberibacter asiaticus' triggers immune responses in solanaceous plants, while csp22 from the solanaceous pathogen 'Candidatus Liberibacter solanacearum' does not. Our findings suggest that vector-borne plant pathogenic bacteria evolved to evade host recognition.

Molecular insights into OPR gene family in Saccharum identified a ScOPR2 gene could enhance plant disease resistance.

12-Oxo-phytodienoic acid reductases (OPRs) perform vital functions in plants. However, few studies have been reported in sugarcane (Saccharum spp.), and it is of great significance to systematically investigates it in sugarcane. Here, 61 ShOPRs, 32 SsOPRs, and 36 SoOPRs were identified from R570 (Saccharum spp. hybrid cultivar R570), AP85-441 (Saccharum spontaneum), and LA-purple (Saccharum officinarum), respectively. These OPRs were phylogenetically classified into four groups, with close genes similar structures. During evolution, OPR gene family was mainly expanded via whole-genome duplications/segmental events and predominantly underwent purifying selection, while sugarcane OPR genes may function differently in response to various stresses. Further, ScOPR2, a tissue-specific OPR, which was localized in cytoplasm and cell membrane and actively response to salicylic acid (SA), methyl jasmonate, and smut pathogen (Sporisorium scitamineum) stresses, was cloned from sugarcane. In addition, both its transient overexpression and stable overexpression enhanced the resistance of transgenic plants to pathogen infection, most probably through activating pathogen-associated molecular pattern/pattern-recognition receptor-triggered immunity, producing reactive oxygen species, and initiating mitogen-activated protein kinase cascade. Subsequently, the transmission of SA and hypersensitive reaction were triggered, which stimulated the transcription of defense-related genes. These findings provide insights into the function of ScOPR2 gene for disease resistance.

Strategies for the biocontrol Pseudomonas infections pre-fruit harvest.

The efficiency of global crop production is under threat from microbial pathogens which is likely to be worsened by climate change. Major contributors to plant disease are Pseudomonas syringae (P. syringae) pathovars which affect a variety of important crops. This opinion piece focuses on P. syringae pathovars actinidiae and syringae, which affect kiwifruit and stone fruits, respectively. We discuss some of the current control strategies for these pathogens and highlight recent research developments in combined biocontrol agents such as bacteriophages and combinations of bacteriophages with known anti-microbials such as antibiotics and bacteriocins.

Modelling the front dynamics of invasive plant pathogens through the analysis of spatial gradients

AbstractVariables of phytopathological interest correlated to the impact of plant diseases, such as incidence and severity, may display a spatial pattern resulting from an underlying, yet unknown gradient. Along the main direction of the gradient the variable assessed at the site level either increases, or decreases. Spatial gradients may also arise because of the movement of a front of invasion, an imaginary moving contour separating areas already infested by a plant pathogen from those still pathogen-free. Adequate geostatistical tools may shed light on gradients directional properties, as well as on the direction the front of invasion is coming from or moving to. Tools currently available for that may be impractical due to the advanced computational and programming skills required for their application. Hence, the goals of this study were: (I) to develop, test and validate a new user-friendly geostatistical tool named DirGrad (Direction of Gradient) aimed at analyzing spatial gradients resulting from the impact of plant diseases; (II) to build an algorithm able to run DirGrad on R, one the most widespread open source software for statistics; and (III) to apply DirGrad for the ex post modelling of the invasion front dynamics. The designed algorithm was successfully validated both in silico and in the field by using data from real case studies such as those of the invasive fungal pathogens Heterobasidion irregulare and Ophiostoma novo-ulmi in a forest stand of Central Italy and across the Swedish island of Gotland, respectively. The algorithm is released as a user-friendly open-source script.

The GhEB1C gene mediates resistance of cotton to Verticillium wilt.

The GhEB1C gene of the EB1 protein family functions as microtubule end-binding protein and may be involved in the regulation of microtubule-related pathways to enhance resistance to Verticillium wilt. The expression of GhEB1C is induced by SA, also contributing to Verticillium wilt resistance. Cotton, as a crucial cash and oil crop, faces a significant threat from Verticillium wilt, a soil-borne disease induced by Verticillium dahliae, severely impacting cotton growth and development. Investigating genes associated with resistance to Verticillium wilt is paramount. We identified and performed a phylogenetic analysis on members of the EB1 family associated with Verticillium wilt in this work. GhEB1C was discovered by transcriptome screening and was studied for its function in cotton defense against V. dahliae. The RT-qPCR analysis revealed significant expression of the GhEB1C gene in cotton leaves. Subsequent localization analysis using transient expression demonstrated cytoplasmic localization of GhEB1C. VIGS experiments indicated that silencing of the GhEB1C gene significantly increased susceptibility of cotton to V. dahliae. Comparative RNA-seq analysis showed that GhEB1C silenced plants exhibited altered microtubule-associated protein pathways and flavonogen-associated pathways, suggesting a role for GhEB1C in defense mechanisms. Overexpression of tobacco resulted in enhanced resistance to V. dahliae as compared to wild-type plants. Furthermore, our investigation into the relationship between the GhEB1C gene and plant disease resistance hormones salicylic axid (SA) and jasmonic acid (JA) revealed the involvement of GhEB1C in the regulation of the SA pathway. In conclusion, our findings demonstrate that GhEB1C plays a crucial role in conferring immunity to cotton against Verticillium wilt, providing valuable insights for further research on plant adaptability to pathogen invasion.

Harnessing RNA interference for the control of Fusarium species: A critical review.

Fusarium fungi are a pervasive threat to global agricultural productivity. They cause a spectrum of plant diseases that result in significant yield losses and threaten food safety by producing mycotoxins that are harmful to human and animal health. In recent years, the exploitation of the RNA interference (RNAi) mechanism has emerged as a promising avenue for the control of Fusarium-induced diseases, providing both a mechanistic understanding of Fusarium gene function and a potential strategy for environmentally sustainable disease management. However, despite significant progress in elucidating the presence and function of the RNAi pathway in different Fusarium species, a comprehensive understanding of its individual protein components and underlying silencing mechanisms remains elusive. Accordingly, while a considerable number of RNAi-based approaches to Fusarium control have been developed and many reports of RNAi applications in Fusarium control under laboratory conditions have been published, the applicability of this knowledge in agronomic settings remains an open question, and few convincing data on RNAi-based disease control under field conditions have been published. This review aims to consolidate the current knowledge on the role of RNAi in Fusarium disease control by evaluating current research and highlighting important avenues for future investigation.

New emerging Pseudomonads as causal agents of bean blight disease

Common bean (Phaseolus vulgaris) production is negatively affected by several bacterial plant diseases. A severe outbreak of bean blight was observed in the nine major bean-producing provinces of Iran during 2021–2022. Necrotic spots were observed on leaves which become necrotic with chlorosis beyond it. One hundred-sixty bacterial strains with a greyish-white colony appearance were consistently isolated from the one-hundred eighteen symptomatic bean leaf, pod, seed and root samples on nutrient agar. All strains were screened for their ability to cause disease bean leaves among which 134 strains induced chlorosis three days after inoculation on bean leaves of cv. Yaghoot followed by necrotic spots on the inoculated sites one week after inoculation. The pathogenic strains clustered in three groups based on phenotypic characters and multilocus sequencing analyses. Three housekeeping genes including gyrB, rpoB, and rpoD of representative strains were partially sequenced. In the phylogenetic tree based on concatenated sequences of these three genes or each gene individually, the strains were divided into three clusters each with high bootstrap support. The first group of the strains clustered with Pseudomonas simiae CCUG 50988T. This is the first report of this species as causal agent of bean blight. The second group clustered close to Pseudomonas alloputida NMI5768T which apparently belong to a new species. The third group clustered in a separate clade in Pseudomonas chlororaphis species which belongs to a new subspecies based on pathogenicity on bean, phenotypic characters, sequences of five housekeeping genes which named as subsp. phaseoli subsp. nov. with the type strain UT-M315.

Isolation of a novel Bacillus subtilis HF1 strain that is rich in lipopeptide homologs and has strong effects on the resistance of plant fungi and growth improvement of broilers.

Bacillus subtilis is an important probiotic microorganism that secretes a variety of antimicrobial compounds, including lipopeptides, which are a class of small molecule peptides with important application value in the fields of feed additives, food, biopesticides, biofertilizers, medicine and the biological control of plant diseases. In this study, we isolated a novel B. subtilis HF1 strain that is rich in lipopeptide components and homologs, has a strong antagonistic effect on a variety of plant fungi, and is highly efficient in promoting the growth of broilers. The live B. subtilis HF1 and its fermentation broth without cells showed significant inhibitory effects on 20 species of plant fungi. The crude extracts of lipopeptides in the fermentation supernatant of B. subtilis HF1 were obtained by combining acid precipitation and methanol extraction, and the lipopeptide compositions were analyzed by ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The results showed that HF1 could produce 11 homologs of surfactin and 13 homologs of fengycin. Among the fengycin homologs, C13-C19 fengycin A and C15-C17 fengycin B were identified; among the surfactin homologs, C11-C17 surfactin A and C13-C16 surfactin B were characterized. C13 fengycin A, C11 surfactin A and C17 surfactin A were reported for the first time, and their functions are worthy of further study. In addition, we found that HF1 fermentation broth with and without live cells could be used as a feed additive to promote the growth of broilers by significantly increasing body weight up to 15.84%. HF1 could be a prospective strain for developing a biocontrol agent for plant fungal diseases and an efficient feed additive for green agriculture.

Interspecific allelopathic interaction primes direct and indirect resistance in neighboring plants within agroforestry systems

The agroforestry system with high biodiversity enhances ecosystem stability and reduces vulnerability to environmental disturbances and diseases. Investigating the mechanisms of interspecies allelopathic interactions for disease suppression in agroforestry offers a sustainable strategy for plant disease management. Here, we used Panax ginseng cultivated under Pinus koraiensis forests, which have low occurrences of Alternaria leaf spot, as a model to explore the role of allelochemicals in disease suppression. Our findings demonstrate that foliar application of P. koraiensis needle leachates effectively enhanced the resistance of P. ginseng against Alternaria leaf spot. Using gas chromatography-mass spectrometry, we identified and quantified endo-borneol as a key compound in P. koraiensis leachates and confirmed its ability to prime resistance in neighboring P. ginseng plants. We discovered that endo-borneol not only directly activates defense-related pathways in P. ginseng to confer resistance but also indirectly recruits its beneficial rhizospheric microbiota by promoting the secretion of ginsenosides, thereby triggering induced systemic resistance. Notably, higher concentrations of endo-borneol, ranging from 10 to 100 mg/l, have a greater capacity to induce plant resistance and enhance root secretion, thereby recruiting more microbiota compared to lower concentrations ranging from 0.01 to 1 mg/l. Additionally, endo-borneol exhibits antifungal activities against the growth of the pathogen Alternaria panax when concentrations exceeded 10 mg/l. These results reveal the multifaceted functions of allelochemical endo-borneol in disease suppression within agroforestry systems and highlight its potential as an environmentally friendly agent for sustainable agriculture.

Ralstonia solanacearum Infection Drives the Assembly and Functional Adaptation of Potato Rhizosphere Microbial Communities.

Bacterial wilt caused by Ralstonia solanacearum is a destructive disease that affects potato production, leading to severe yield losses. Currently, little is known about the changes in the assembly and functional adaptation of potato rhizosphere microbial communities during different stages of R. solanacearum infection. In this study, using amplicon and metagenomic sequencing approaches, we analyzed the changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere across four stages of R. solanacearum infection. The results showed that R. solanacearum infection led to significant changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere, with various microbial properties (including α,β-diversity, species composition, and community ecological functions) all being driven by R. solanacearum infection. The relative abundance of some beneficial microorganisms in the potato rhizosphere, including Firmicutes, Bacillus, Pseudomonas, and Mortierella, decreased as the duration of infection increased. Moreover, the related microbial communities played a significant role in basic metabolism and signal transduction; however, the functions involved in soil C, N, and P transformation weakened. This study provides new insights into the dynamic changes in the composition and functions of potato rhizosphere microbial communities at different stages of R. solanacearum infection to adapt to the growth promotion or disease suppression strategies of host plants, which may provide guidance for formulating future strategies to regulate microbial communities for the integrated control of soil-borne plant diseases.

Cassava crop disease prediction and localization using object detection

In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed result. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.

Two medicinal molecules Hydralazine and Isoniazid have the potential to control wheat crown rot by combing with a N-acetyltransferase FDB2

Fusarium pseudograminearum is the main pathogen that causes wheat crown rot (WCR), causing serious harm to wheat production. Wheat secretes Benzoxazolinones (Bxs) as fungicidins to prevent F. pseudograminearum infection. Fusarium Detoxification of Bx 2 (FDB2) can degrade Bx to non-fungitoxic N-(2-hydroxyphenyl) malonamic acid. Therefore, FDB2 may be a potential drug target for WCR.In the present study, the structure of FDB2 was determined using the molecular replacement method. The overall FDB2 structure displayed a typical N-acetyltransferase (NAT1) conformation. Unlike other NAT1s, the active site cleft is divided into two parts by a long loop (A135MSPYPDVRKNQA147). Hydralazine, Isoniazid, and 2,4′-dibromoacetanilide were screened out as potential inhibitors of FDB2 by structure alignment. Affinity measurements by MST showed that FDB2 prefers to combine Isoniazid and Hydralazine rather than its natural substrate, 2-aminophenol. Wheat seedling infection assays showed that Isoniazid and Hydralazine suppress F. pseudograminearum invasion in wheat. Our study found that Hydralazine and Isoniazid have the potential to control WCR. This article provides a new idea for the application of medicine, which has serious adverse effects, on plant disease control to reduce research costs and make obsolete drugs shine with vitality.

Zero shot plant disease classification with semantic attributes

In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of early and accurate disease identification. This paper explores the integration of advanced deep learning techniques, including pre-trained models, zero-shot learning, and semantic attributes to enhance the effectiveness of plant disease detection systems. High level features extracted from the images by these pretrained models capture crucial patterns, while domain-specific semantic attributes, such as leaf texture and color variations, enhance the understanding. Incorporating zero-shot learning enables adaptation to new and unseen diseases using semantic descriptions. Experimental validation across diverse plant species and disease types underscores the approach’s reliability in real-world agricultural scenarios. Our approach has demonstrated superior performance with plant village dataset, showing a significant improvement in accuracy and generalization. These results underscore the potential of our method to revolutionize plant disease detection and management in agricultural practices.

A systematic review of deep learning techniques for plant diseases

Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

Multi-scale and multi-receptive field-based feature fusion for robust segmentation of plant disease and fruit using agricultural images

The accurate and fast assessment of plant diseases and fruits is important for sustainable and productive agriculture. However, typically manual methods are adopted for the assessment of plant diseases and fruit, which is a time-consuming, resource-intensive, and error-prone approach. A few artificial intelligence (AI)-based methods have been introduced to automate this process, but they show limitations in attaining high performance in challenging imaging conditions such as occlusions, poor lighting, noise, indistinctive boundaries, and excessive morphological variations. Moreover, current approaches also lack in providing computationally efficient solutions. To overcome these issues, two novel architectures are developed to segment plant diseases and fruits with higher segmentation performance and less computational requirements. The efficient feature fusion segmentation network (EFFS-Net) is the base network, and a multi-scale dilated feature fusion segmentation network (MDFS-Net) is the final network of this study. EFFS-Net uses an identity skip path-based feature fusion mechanism with an efficient grouped convolutional depth (EGCD) to provide satisfactory segmentation performance with high computational efficiency. MDFS-Net uses a multi-scale feature fusion mechanism and fuses low-level information in the EGCD and other sections of the network to learn detailed input features for delivering promising performance even in challenging imaging conditions. MDFS-Net also applies multiple receptive fields to the low-level information in the effective receptive field processing (ERFP) block for fusion near the pixel classification stage for further performance enhancement.Both networks are evaluated using a Brazilian Arabica coffee leaf (BRACOL) image dataset, an Australian Center for Field Robotics orchard fruit (apple) dataset, and a necrotized cassava root cross-section image dataset. The proposed method provides a promising segmentation performance, achieving dice similarity coefficients of 88.81 %, 95.01 %, and 86.04 % with performance improvement of 3.5 %, 2.6 %, and 1.07 % on the three datasets, respectively, with approximately ten times less number of required trainable parameters compared with the state-of-the-art methods.

Application of loop-mediated isothermal amplification (LAMP) in plant pathogen detection.

Plant diseases impact the production of all kinds of crops, resulting in significant economic losses worldwide. Timely and accurate detection of plant pathogens is crucial for surveillance and management of plant diseases. In recent years, loop-mediated isothermal amplification (LAMP) has become a popular method for pathogen detection and disease diagnosis due to the advantages of its simple instrument requirement and constant reaction temperature. In this review, we provide an overview of current research on LAMP, including the reaction system, design of primers, selection of target regions, visualization of amplicons, and application of LAMP on the detection of all major groups of plant pathogens. We also discuss plant pathogens for which LAMP is yet to be developed, potential improvements of plant disease diagnosis, and disadvantages that need to be considered.