Anthracnose caused by Colletotrichum scovillei is a major constraint on red pepper (Capsicum annuum) production in South Korea. Intensive use of quinone outside inhibitor (QoI) fungicides has led to widespread resistance, mainly associated with the G143A mutation in the cytochrome b gene. Rapid and reliable resistance monitoring is required to support sustainable disease management. This study evaluated quantitative sequencing (QS) as a molecular tool for detecting and quantifying QoI resistance allele frequencies. Genomic DNA and spore suspensions of pyraclostrobin-sensitive and -resistant isolates were mixed at known ratios to generate pseudo-populations. QS accurately reflected the expected G143A allele frequencies, showing nearly perfect linear correlations (R2 > 0.99) for both genomic DNA and spore pools. The applicability of QS was further confirmed using artificially inoculated pepper fruits, where allele frequencies determined from lesion-derived DNA were consistent with inoculum ratios. Field surveys conducted from 2020 to 2023 revealed that the frequency of pyraclostrobin-resistant isolates increased from 72.0% to 91.6%. Resistance frequencies obtained by QS were strongly correlated with those determined by the conventional mass agar dilution method (R2 = 0.82–0.97), validating QS as a high-throughput monitoring tool. These results demonstrate that QS provides a robust, sensitive, and scalable approach for monitoring fungicide resistance in C. scovillei populations and can be implemented to guide fungicide use strategies and delay further resistance development.

Fire blight, caused by Erwinia amylovora, is an economically devastating disease affecting apple and pear orchards, and reliable detection is critical for effective management. However, field detection is challenging due to inhibitory compounds and the time-consuming nature of nucleic acid extraction, which limits the speed and accessibility of current diagnostic methods. Here, we present a CRISPR-Cas13a-based diagnostic platform designed for rapid, amplification-free, and extraction-free detection directly from plant material. In regions such as Korea where E. pyrifoliae is endemic, high genomic similarity between the two Erwinia species complicates accurate discrimination and poses a significant challenge for disease management. We identified E. amylovora-specific (EA-specific) single nucleotide polymorphisms and designed a panel of CRISPR RNAs (crRNAs) across multiple housekeeping genes and the 16S rRNA V3 region. Systematic screening with both synthetic RNA and mRNA revealed new crRNAs that maintained species specificity and sensitivity, achieving detection within minutes. To enable field-compatible sample processing, we developed and optimized a robust alkaline lysis workflow based on sequential NaOH lysis and HCl neutralization, which effectively released RNA from bacterial cells and remained compatible with crude Malus domestica leaf lysates. Under these extraction-free conditions, the assay achieved rapid, EA-specific detection of 1 × 106 CFUs/reaction within 15 minutes without nucleic acid purification or thermal cycling in the presence of plant material. This study establishes a practical framework for CRISPR-Cas13a diagnostics in plant pathology and provides a low-infrastructure strategy that can improve the speed and accuracy of fire blight surveillance and broader agricultural biosecurity efforts.

Soybean (Glycine max [L.] Merr.) is a globally important crop; however, its productivity is severely constrained by the soybean cyst nematode (Heterodera glycines Ichinohe). This nematode often remains undetected during early infection and persists in the soil as dormant cysts, causing long-term yield losses. Although conventional detection methods, such as microscopic inspection and polymerase chain reaction assays, provide accuracy, they are labor-intensive and unsuitable for large-scale monitoring. Therefore, an artificial intelligence-based framework was established for the classification and segmentation of female soybean cyst nematodes using advanced deep learning architectures. Soil samples were collected from infected fields in South Korea and female nematodes were imaged with red–green–blue cameras under a dissecting microscope. Instance segmentation was benchmarked across YOLOv5, YOLOv8, YOLOv11, and Detectron2. The fine-tuned YOLOv11 model achieved the best performance, with a precision of 0.977, a recall of 0.980, and a mean Average Precision at 50% intersection-over-union of 0.988. Additionally, color-based phenotyping using hue–saturation–value thresholds classified 4,392 nematode images into yellow, orange, and brown groups, representing the reproductive and developmental stages. Consequently, this integrated framework highlights the potential of artificial intelligence-driven detection systems to reduce labor-intensive practices and support sustainable soybean production through the improved management of nematode-induced yield losses.

Understanding and predicting epidemiological trends of plant viruses is essential for sustaining crop productivity and control strategies. The National Center for Biotechnology Information (NCBI) GenBank provides nucleotide sequences with metadata such as date, location, and host, offering valuable resources for research. However, GenBank lacks automated tools for visualizing temporal and spatial patterns. To address this limitation, we applied a vibe coding approach, a generative AI assisted method that enables non-programmers to process and visualize data efficiently. As a case study, we analyzed pepper (Capsicum spp.), a major East and Southeast Asian crop threatened by emerging viruses. Using vibe coding, we visualized reporting trends by country and year and mapped sequence variation and conserved regions of pepper-infecting viruses. This approach allowed rapid organization of large datasets and real-time utilization of newly deposited GenBank entries. NCBI-based plant virus analysis system provides automated analysis and visualization and is accessible at https://plantvirus-viewer.duckdns.org/.

Hypomontagnella monticulosa is an emerging pathogen of Pachira glabra Pasq. causing white leaf spot, a damaging fungal disease of P. glabra in southern China. The early and proper detection and qualification is of fundamental for understanding epidemiology and developing preventive measures of this fungus. Using the second largest subunit of the RNA polymerase II gene as target, a quantitative TaqMan real-time polymerase chain reaction assay was developed for the detection and quantification of H. monticulosa in P. glabra leaves. This method could specifically recognize all tested H. monticulosa strains, while no cross-reaction was observed in closely related Hypoxylon species. Sensitivity of the assays was determined to be as low as 0.05 fg/μL (2, 300 copies/μL) of plasmid DNA, 0.5 pg/μL of mycelia genomic DNA, and 0.001% of target DNA mixed with leaf tissue DNA. The two-stage induction of H. monticulosa DNA was observed during the infection process, suggesting that this assay could be used to monitor the growth dynamics of this fungus in the whole disease process. Additionally, the assay could not only effectively detected H. monticulosa in naturally infected P. glabra trees in fields, but also accurately evaluate the differences in resistance among varieties of P. glabra. Therefore, the current study provides a rapid and accurate technology for monitoring and qualification of H. monticulosa infection in P. glabra, and will be applicable for prediction and control of the disease but also for the study of plant-H. monticulosa interaction.

Rice bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) remains a serious yield threat. To identify selective antibacterials, we synthesized a small library of naphthalene-2-acyl imidazolium salts (NAIMSs) and evaluated their anti-Xoo activity. Disk-diffusion assays of 18 analogues revealed a clear structure-activity relationship: isopentyloxy-substituted derivatives were active, whereas others were not. Among them, NAIMS1f exhibited potent inhibition with a minimum inhibitory concentration of 0.75 μg mL−1. Growth kinetics demonstrated bacteriostatic action, completely suppressing low-inoculum growth but not killing high-density cultures. NAIMS1f showed higher sensitivity toward Xoo than Escherichia coli, Bacillus pumilus, or Pseudomonas syringae, defining a useful sensitivity window. These findings identify NAIMS1f as a promising lead for Xoo-focused inhibition in vitro and provide a basis for future mechanistic and pre-field validation studies.

Soil-borne phytopathogenic fungi cause root rot, wilt, and damping-off in crops, leading to major yield losses worldwide. Because symptoms appear only after underground infection progresses, early detection is crucial. Here, a rapid 20-min genomic DNA extraction method was developed for eight pathogens—Alternaria tenuissima, Botryosphaeria dothidea, Fusarium oxysporum, Glomerella cingulata, Phytophthora cactorum, Rosellinia necatrix, Sclerotium rolfsii, and Sclerotinia sclerotiorum. The protocol uses a cetyltrimethylammonium bromide-based buffer, steel and glass beads, brief heating (95°C, 1 min), vortexing, and sequential purification with Q-Sepharose and magnetic beads. All pathogens were detected within 30 quantitative polymerase chain reaction cycles, while soil-only controls exceeded 30 Cq. Sclerotial DNA of S. rolfsii (Cq ≈ 25) was also detected, confirming applicability for overwintering inocula. This simple and low-cost protocol enables rapid, reliable detection of multiple soil-borne fungi directly from soil, providing a practical tool for on-site disease diagnosis and management.

Plant viruses severely limit global crop productivity, yet no pesticide-like antiviral agents are currently available for effective control. Double-stranded RNA (dsRNA) technologies, acting through RNA interference, provide a sequence-specific and non-transgenic strategy to suppress viral replication and have emerged as promising non-transgenic solutions for crop protection. Spray-induced gene silencing (SIGS), which applies externally produced dsRNA through foliar sprays, seed treatments, or root uptake, provides practical advantages over genetic modification, including rapid deployment, environmental compatibility, and target specificity. Recent advances in industry-scale dsRNA production and nanomaterial-based formulations have improved dsRNA stability, uptake, and persistence in planta, supporting the feasibility of field application. However, major challenges persist, such as rapid environmental degradation, restricted systemic mobility, high production costs, and unresolved biosafety and regulatory issues. Overcoming these challenges will require innovations in cost-effective and scalable in vitro RNA production, protective formulations, and precision delivery technologies, alongside comprehensive ecological risk assessments. Finally, this review emphasizes current technological advances of SIGS, integrating with nanotechnology and other reliable field application methodologies. Taken together, these advances position dsRNA-based technologies as a realistic and transformative platform for next-generation, sustainable plant virus management.

Plant pathogenic fungi have evolved molecular arsenals that enable them to successfully invade their host plants and ultimately achieve colonization. These mechanisms involve multifaceted and complex processes that require spatiotemporal regulation of various genes. The cosmopolitan genus Fusarium has been recognized worldwide as an important group of plant pathogens that exhibit diverse virulence mechanisms. This review seeks a broad overview of (a) how the virulence factors and their regulatory mechanisms are specifically utilized during the early invasion process in Fusarium species, (b) the gene regulatory mechanisms that govern this process, and (c) future directions for molecular genetics in disease control by directly targeting virulence factors in plant pathogenic fungi. By integrating current knowledge of key virulence factors and intrinsic mechanisms in Fusarium–plant systems, this work aims to provide a comprehensive understanding of Fusarium-mediated pathogenesis, with particular emphasis on the early stages of infection. Finally, we outline the shared and species-specific contributions of virulence factors, integrating findings from previous studies across individual Fusarium species.

Cassava mosaic disease, caused by Cassava mosaic begomoviruses in the family Geminiviridae, poses a major threat to cassava production, with Sri Lankan cassava mosaic virus (SLCMV) being the dominant strain in Southeast Asia. Transmitted via infected propagative stems and whiteflies (Bemisia tabaci), SLCMV’s impact on cassava metabolite dynamics remains poorly understood. This study investigated metabolite profile changes in resistant, tolerant, and susceptible cassava cultivars at 1, 3, and 7 days after inoculation by viruliferous whiteflies. Distinct metabolite patterns were observed among cultivars, with several pathways linked to plant defense identified, including flavonoid biosynthesis, phenylpropanoid biosynthesis, and purine metabolism. Secondary metabolite pathways, such as the energy-signaling SnRK1/AMPK-liked proteins, alpha-linolenic acid metabolism, and starch and sucrose metabolism, were also implicated. The results provide insights into metabolite-mediated defense mechanisms during early and late infection, enhancing understanding of cassava’s responses to SLCMV inoculation after exposure to viruliferous whitefly infestation. This study supports the development of SLCMV-resistant cassava cultivars.