Search Results (1,162)

Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study presents a systematic benchmark of five deep convolutional neural networks (CNNs)—Visual Geometry Group (VGG)16, VGG19, Residual Network (ResNet)101V2, Xception, and Densely Connected Convolutional Network (DenseNet)121—for rice disease identification using a public leaf image dataset. The models, initialized with ImageNet pre-trained weights, were rigorously evaluated under a unified framework, including 5-fold cross-validation and a challenging out-of-distribution (OOD) generalization test. Our results demonstrate a clear performance hierarchy, with DenseNet121 emerging as the superior model. It achieved the highest OOD accuracy and F1-score (both 85.08%) while exhibiting the greatest parameter efficiency (8.1 million parameters), making it ideally suited for edge deployment. In contrast, architectures with large fully connected layers (VGG) or less efficient feature learning mechanisms (Xception, ResNet101V2) showed lower performance in this specific task. This study confirms the critical impact of architectural design choices, provides a reproducible performance baseline, and identifies DenseNet121 as a robust, efficient, and highly recommendable CNN for practical rice disease diagnosis in precision agriculture. Full article

The crude ethyl acetate extract of the culture of a marine sponge-associated fungus, Aspergillus unguis KUFA 0098, was tested for its capacity to inhibit the growth of ten phytopathogenic fungi, viz. Alternaria brassicicola, Bipolaris oryzae, Colletotrichum capsici, Curvularia oryzae, Fusarium semitectum, Lasiodiplodia theobromae, Phytophthora palmivora, Pyricularia oryzae, Rhizoctonia oryzae, and Sclerotium roflsii. At a concentration of 1 g/L, the crude extract was most active against P. palmivora, causing the highest growth inhibition (55.32%) of this fungus but inactive against R. oryzae and S. roflsii. At a concentration of 10 g/L, the crude extract completely inhibited the growth of most of the fungi, except for L. theobromae, R. oryzae, and S. roflsii, with 94.50%, 74.12%, and 67.80% of inhibition, respectively. The crude extract of A. unguis KUFA 0098 exhibited growth-inhibitory effects against B. oryzae and P. oryzae, causative agents of brown leaf spot disease and leaf blast disease, respectively, on rice plant var. KDML105, under greenhouse conditions. Chromatographic fractionation and purification of the extract led to the isolation of four previously described depsidones, viz. unguinol (1), 2-chlorounguinol (2), 2,4-dichlorounguinol (3), and folipastatin (4), as well as one polyphenol, aspergillusphenol A (5). The major compounds, i.e., 1, 2, and 4, were tested against the ten phytopathogenic fungi. Compounds 1 and 4 were able to inhibit growth of most of the fungi, except L. theobromae, R. oryzae, and S. roflsii. Compound 1 showed the same minimum inhibitory concentration (MIC) values as that of carbendazim against A. brassicicola, C. capsici, C. oryzae, and P. oryzae, while compound 4 showed the same MIC values as that of carbendazim against only C. capsici and P. oryzae. Compound 2 was not active against all of the ten phytopathogenic fungi tested. Full article

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a Pixel Color- and Coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing the Mahalanobis distance metric, aimed at accurately segmenting symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC, combined with manual classification of the segmented regions, was evaluated using Lin’s concordance correlation coefficient (${\rho }_c$) by comparing its results to visual measurements of ShB lesion height (cm) and to lesion/diseased area (cm₂) measured using ImageJ. Low bias (C_b) and high precision (r) were observed for absolute lesion height (C_b = 0.93, r = 0.94) and absolute symptomatic area (C_b = 0.98, r = 0.97) studies. Furthermore, to automatically classify the segmented regions produced by the PCC-KMC algorithm, we employed a convolutional neural network (CNN). Unlike conventional CNNs that require fixed-size image inputs, our CNN is designed to take the RGB histogram of each segmented region (a 1000 by 3 representation) as input and determine whether the region corresponds to ShB infection. This design effectively handles the arbitrary sizes and irregular shapes of segmentation regions generated by PCC-KMC. Our CNN was trained based on an 85%:15% composition for the training and testing dataset from a total of 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (C_b = 0.86, r = 0.90) and absolute diseased area (C_b = 0.99, r = 0.97) studies, indicating that PCC-KMC combined with automatic CNN-based classification performs very effectively. These results demonstrate that the potential of our methodology to serve as an alternative to the traditional visual-based ShB disease severity assessment and can be considered to be utilized for lab-scale, high-throughput phenotyping of rice ShB. Full article

Biological control is an effective and environmentally friendly strategy for managing plant diseases. In this study, a broad-spectrum antagonistic bacterium, designated strain AN6, was isolated from rice plants and exhibited potent inhibitory activity against a variety of phytopathogens. In Oxford cup assays, AN6 suppressed the growth of Xanthomonas oryzae pv. oryzae (Xoo) by 73.60%, and its cell-free culture filtrate caused pronounced morphological deformation in the bacterial cells. Further in vitro assays, including dual-culture assays, volatile organic compound (VOC) assays, and cell-free supernatant (CFS) assays, demonstrated that AN6 also exerted strong antifungal effects against several pathogenic fungi. In addition, the strain was found to produce proteases and siderophores, which may contribute to its antagonistic capabilities. Taxonomic identification based on morphological traits, 16S rRNA and gyrA gene sequencing, average nucleotide identity (ANI), in silico DNA–DNA hybridization (isDDH), and phylogenetic analysis classified strain AN6 as Bacillus velezensis. Whole-genome sequencing revealed that AN6 harbors a 3,929,788 bp genome comprising 4025 protein-coding genes with a GC content of 46.50%. Thirteen biosynthetic gene clusters (BGCs) associated with the production of secondary metabolites—such as nonribosomal peptides, polyketides, and dipeptide antibiotics—were identified. The pot experiment further validated the biocontrol potential of AN6, achieving an 80.49% reduction in rice bacterial blight caused by Xanthomonas oryzae pv. oryzae. Collectively, these results indicate that B. velezensis AN6 is a promising candidate for development as a highly effective biocontrol agent for the integrated management of diverse plant diseases. Full article

Rice brown spot (BS) disease, caused by Bipolaris oryzae, is a significant threat to rice production worldwide. In this study, a major quantitative trait locus (QTL), qBS11, associated with resistance to BS in rice, was identified and fine-mapped. A recombinant inbred line (RIL) population from a cross between the susceptible variety Zhenshan97 and the resistant variety C309 was used for QTL mapping. Using composite interval mapping (CIM) and bulked segregant analysis sequencing (BSA-seq), qBS11 was narrowed to a 244.6 kb interval on chromosome 11, explaining up to 47.7% of the phenotypic variance. Fine mapping identified several potential candidate genes, including LOC_Os11g41170 and LOC_Os11g41210, encoding disease resistance proteins. The resistance exhibited by qBS11 was found to be partially dominant, with heterozygotes showing medium resistance. High broad-sense heritability (89.2%) confirmed the dominance of genetic factors in BS resistance. Additionally, regulatory region variations in the candidate genes suggest a gene dosage effect, which may explain the partial dominance observed for qBS11. This study provides valuable insights into the genetic basis of BS resistance and offers a foundation for breeding BS-resistant rice varieties through molecular marker-assisted selection (MAS). The findings also pave the way for future functional studies of the identified genes. Full article

Rice sheath blight caused by Rhizoctonia solani is a devastating global rice disease. This study aimed to isolate biocontrol bacteria from the medicinal plant Bletilla striata for managing the disease. Strain A1 demonstrated the strongest antagonistic activity, with a 91.92% inhibition rate against R. solani in vitro. It also exhibited a broad antifungal spectrum against ten plant pathogenic fungi. Morphological and molecular (16S rRNA and recA genes) analysis identified strain A1 as Pseudomonas koreensis. In detached leaf assays, lesion length was significantly reduced. Pot and field trials showed control efficacies of 65.54% and 72.53%, respectively, comparable to the chemical agent Jinggangmycin. Strain A1 secreted extracellular enzymes (protease, β-1,3-glucanase), siderophores, and auxin (IAA), and possessed phosphate-solubilizing and nitrogen-fixing capabilities. The strain significantly enhanced the activities of key defense enzymes (POD, PAL, PPO, CAT, SOD) in rice. Furthermore, both its sterile culture filtrate and the corresponding crude ethyl acetate extract exhibited strong, direct suppression of R. solani growth. LC-MS analysis identified potential antifungal compounds, including Pseudomonic Acid, Artemisinin, and Tetradecane, in the extract. In conclusion, P. koreensis A1 is a promising biocontrol and plant growth-promoting candidate for sustainable management of rice sheath blight. Full article

Straw returning is a critical practice with profound strategic importance for sustainable agricultural development. However, within a comprehensive soil health evaluation framework, research analyzing the impact of tobacco straw returning on soil ecosystem health from the perspectives of microbial taxa and functional genes remains insufficient. To investigate the effects of tobacco straw returning on virulence factor genes (VFGs), methane-cycling genes (MCGs), nitrogen-cycling genes (NCGs), carbohydrate-active enzyme genes (CAZyGs), antibiotic resistance genes (ARGs), and their host microorganisms in soil, this study collected soil samples from a long-term tobacco-rice rotation field with continuous tobacco straw incorporation in Shaowu City, Fujian Province. Metagenomic high-throughput sequencing was performed on the samples. The results demonstrated that long-term tobacco straw returning influenced the diversity of soil VFGs, MCGs, NCGs, CAZyGs, ARGs, and their host microorganisms, with richness significantly increasing compared to the CK treatment (p < 0.05). In the microbially mediated methane cycle, long-term tobacco straw returning resulted in a significant decrease in the abundance of the key methanogenesis gene mttB and the methanogenic archaeon Methanosarcina, along with a reduced mtaB/pmoA functional gene abundance ratio compared to CK. This suggests enhanced CH₄ oxidation in the tobacco-rice rotation field under straw returning. Notably, the abundance of plant pathogens increased significantly under tobacco straw returning. Furthermore, a significantly higher norB/nosZ functional gene abundance ratio was observed, indicating a reduced capacity of soil microorganisms to convert N₂O in the tobacco-rice rotation field under straw amendment. Based on the observation that the full-rate tobacco straw returning treatment (JT2) resulted in the lowest abundances of functional genes prkC, stkP, mttB, and the highest abundances of nirK, norB, malZ, and bglX, it can be concluded that shifts in soil physicochemical properties and energy substrates drove a transition in microbial metabolic strategies. This transition is characterized by a decreased pathogenic potential of soil bacteria, alongside an enhanced potential for microbial denitrification and cellulose degradation. Non-parametric analysis of matrix correlations revealed that soil organic carbon, dissolved organic carbon, alkaline-hydrolyzable nitrogen, available phosphorus, and available potassium were significantly correlated with the composition of soil functional groups (p < 0.05). In conclusion, long-term tobacco straw returning may increase the risk of soil-borne diseases in tobacco-rice rotation systems while potentially elevating N₂O and reducing CH₄ greenhouse gas emission rates. Analysis of functional gene abundance changes identified the full-rate tobacco straw returning treatment as the most effective among all treatments. Full article

Bacterial leaf blight (BLB), a destructive disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo), continues to limit rice productivity worldwide. Although biologically synthesized zinc oxide nanoparticles (ZnO NPs) have been extensively investigated, knowledge regarding the antibacterial activity and biocompatibility of commercially available ZnO NPs is still limited. In this study, commercial ZnO NPs were systematically characterized and evaluated for their antibacterial mechanisms and biocompatibility in mammalian cells. FE-SEM and TEM analyses revealed irregular polyhedral, hexagonal, and short rod-like morphologies with an average particle size of ~33 nm, consistent with crystallite sizes estimated by XRD. The nanoparticles exhibited pronounced antibacterial activity against Xoo, with a minimum inhibitory concentration (MIC) of 16 µg/mL and a clear dose-dependent response. Mechanistic assays confirmed multifaceted bactericidal actions involving membrane disruption, ROS generation, Zn²⁺ release, and ultrastructural damage. Biocompatibility testing in human dermal fibroblasts showed enhanced proliferation at 8–32 µg/mL, no cytotoxicity up to 256 µg/mL, and reduced viability only at ≥512 µg/mL. These findings represent the first mechanistic evaluation of commercial ZnO NPs against Xoo, together with cytotoxicity assessment in mammalian cells, highlighting their structural distinctness and dual functionality that combine potent antibacterial activity with minimal mammalian cytotoxicity. Overall, the results underscore their potential as safe nanobiocontrol agents for sustainable rice disease management. Full article

RNA methylation, particularly N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), functions as a pivotal post-transcriptional regulatory mechanism and plays a central role in plant growth, development, and stress responses. This review provides a systematic summary of recent advances in RNA methylation research in cucurbit crops. To date, high-throughput technologies such as MeRIP-seq and nanopore direct RNA sequencing have enabled the preliminary construction of RNA methylation landscapes in cucurbit species, revealing their potential regulatory roles in key agronomic traits, including fruit development, responses to biotic and abiotic stresses, and disease resistance. Nevertheless, this field remains in its early stages for cucurbit crops and faces several major challenges: First, mechanistic understanding is still limited, with insufficient knowledge regarding the composition and biological functions of the core protein families involved in methylation dynamics—namely, “writers,” “erasers,” and “readers.” Second, functional validation remains inadequate, as direct evidence linking specific RNA methylation events to downstream gene regulation and phenotypic outcomes is largely lacking. Third, resources are scarce; compared to model species such as Arabidopsis thaliana and rice, cucurbit crops possess limited species-specific genetic data and genetic engineering tools (e.g., CRISPR/Cas9-based gene editing systems), which significantly hampers comprehensive functional studies. To overcome these limitations, future research should prioritize the development and application of more sensitive detection methods, integrate multi-omics datasets—including transcriptomic and methylomic profiles—to reconstruct regulatory networks, and conduct rigorous functional assays to establish causal relationships between RNA methylation modifications and phenotypic variation. The ultimate objective is to fully elucidate the biological significance of RNA methylation in cucurbit plants and harness its potential for crop improvement through genetic and biotechnological approaches. Full article

The spotted-leaf mutant, characterized by spontaneous lesion formation resembling pathogen-induced hypersensitive cell death, serves as an ideal model for studying the molecular mechanisms behind rice (Oryza sativa) disease resistance and programmed cell death, as these plants display hypersensitive responses that mimic those triggered by pathogen infection. In this study, we generated a knockout line using CRISPR/Cas9 technology in homologous mutant HM113-induced calli. LOC_Os07g30510 encodes a cysteine-rich receptor kinase with a DUF26 domain, consisting of 688 amino acids. HM113 was localized to the cytosol and expressed in most rice tissues at various growth stages. A single nucleotide substitution from A to T was observed at the 847th base of LOC_Os07g30510, causing an amino acid change from serine to cysteine. Our results demonstrated that the A847T mutation was responsible for the spotted-leaf phenotype in the HM113 mutant through gene editing technology, as new frameshift mutations were introduced upstream of the A847T site in the HM113 gene. The mutation phenotype of HM113 was eliminated and resistance to bacterial blight was also lost, indicating that it is a gain-of-function gene. Full article

Phototropins (PHOTs) are plant blue-light receptors that mediate crucial physiological processes such as phototropism, chloroplast movement, stomatal opening, and flowering. However, the PHOT family genes remain poorly characterized in pepper. Here, we identified and molecularly cloned two PHOT genes (CaPHOT1 and CaPHOT2) in pepper, which were phylogenetically classified into distinct groups with their homologs from rice, maize, tomato, and Arabidopsis. These genes exhibit conserved gene structures, implying functional conservation during evolution. Subcellular localization analysis confirmed that both CaPHOT1 and CaPHOT2 are localized to the plasma membrane. Expression profiling revealed that both CaPHOT1 and CaPHOT2 were expressed in all tissues, with the highest transcripts in leaves and the lowest in roots. Notably, RNA-seq data revealed that the expression of CaPHOT1 was up-regulated by JA and SA, whereas CaPHOT2 showed no significant changes. Furthermore, CaPHOT1 and CaPHOT2 displayed divergent expression patterns upon Phytophthora capsici infection (PCI). Furthermore, transient overexpression of CaPHOT1 in pepper enhanced susceptibility to PCI, indicating its negative role in disease resistance. Our findings identified the CaPHOT gene family in pepper and functionally demonstrated that CaPHOT1 negatively regulates resistance to PCI, thereby providing insights for future research on PHOTs in other plant species. Full article

Background/Objectives: Bacterial blight (BB) represents one of the most devastating diseases threatening global rice production. Exploring and characterizing disease resistance (R) genes provides an effective strategy for controlling BB and enhancing rice resilience. Common wild rice (Oryza rufipogon) serves as a valuable reservoir of genetic diversity and disease resistance resources. In this study, we identified and functionally characterized a novel NLR gene, YPR1, from common wild rice (Oryza rufipogon), which exhibited significant spatial, temporal, and tissue-specific expression patterns. Methods: Using a combination of conventional PCR, RT-PCR, bioinformatics, transgenic analysis, and CRISPR/Cas9 gene-editing approaches, the full-length YPR1 sequence was successfully cloned. Results: The gene spans 4689 bp with a coding sequence (CDS) of 2979 bp, encoding a 992-amino acid protein. Protein domain prediction revealed that YPR1 is a typical CNL-type NLR protein, comprising RX-CC_like, NB-ARC, and LRR domains. The predicted molecular weight of the protein is 112.43 kDa, and the theoretical isoelectric point (pI) is 8.36. The absence of both signal peptide and transmembrane domains suggests that YPR1 functions intracellularly. Furthermore, the presence of multiple phosphorylation sites across diverse residues implies a potential role for post-translational regulation in its signal transduction function. Sequence alignment showed that YPR1 shared 94.02% similarity with Os09g34160 and up to 96.47% identity with its closest homolog in the NCBI database, confirming that YPR1 is a previously unreported gene. To verify its role in disease resistance, an overexpression vector (Ubi–YPR1) was constructed and introduced into the BB-susceptible rice cultivar JG30 via Agrobacterium tumefaciens-mediated transformation. T₁ transgenic lines were subsequently inoculated with 15 highly virulent Xanthomonas oryzae pv. oryzae (Xoo) strains. The transgenic plants exhibited strong resistance to eight strains (YM1, YM187, C1, C5, C6, T7147, PB, and HZhj19), demonstrating a broad-spectrum resistance pattern. Conversely, CRISPR/Cas9-mediated knockout of YPR1 in common wild rice resulted in increased susceptibility to most Xoo strains. Although the resistance of knockout lines to strains C7 and YM187 was comparable to that of the wild type (YP^WT), the majority of knockout plants exhibited more severe symptoms and significantly lower YPR1 expression levels compared with YP^WT. Conclusions: Collectively, these findings demonstrate that YPR1 plays a crucial role in bacterial blight resistance in common wild rice. As a novel CNL-type NLR gene conferring specific resistance to multiple Xoo strains, YPR1 provides a promising genetic resource for the molecular breeding of BB-resistant rice varieties. Full article

Accurate and reliable detection of rice diseases and pests is crucial for ensuring food security. However, traditional deep learning methods often suffer from high rates of missed and false detections when dealing with complex field environments, especially in the presence of tiny disease spots, due to insufficient feature extraction capabilities. To address this issue, this study proposes a high-precision rice disease detection method based on ensemble learning and conducts experiments on a self-built dataset of 12,572 images containing five types of diseases and one type of pest. The ensemble learning model is optimized and constructed through a phased approach: First, using YOLOv8s as the baseline, transfer learning is performed with the agriculture-related dataset PlantDoc. Subsequently, a P2 small-object detection head, an EMA mechanism, and the Focal Loss function are introduced to build an optimized single model, which achieves an mAP_0.5 of 0.899, an absolute improvement of 5.5% compared to the baseline YOLOv8s. Then, three high-performance YOLO object detection models, including the improved model mentioned above, are selected, and the Weighted Box Fusion technique is used to integrate their prediction results to construct the final Ensemble-WBF model. Finally, the AP_0.5 and AR_0.5:0.95 of the model reach 0.922 and 0.648, respectively, with absolute improvements of 2.2% and 3.2% compared to the improved single model, further reducing the false and missed detection rates. The experimental results show that the ensemble learning method proposed in this study can effectively overcome the interference of complex backgrounds, significantly improve the detection accuracy and robustness for tiny and similar diseases, and reduce the missed detection rate, providing an efficient technical solution for the accurate and automated monitoring of rice diseases in real agricultural scenarios. Full article

Rice, a dietary staple for over half of the global population, is highly susceptible to bacterial and fungal diseases such as bacterial blight, brown spot, and leaf smut, which can severely reduce yields. Traditional manual detection is labor-intensive and often results in delayed intervention and excessive chemical use. Although deep learning models like convolutional neural networks (CNNs) achieve high accuracy, their computational demands hinder deployment in resource-limited agricultural settings. We propose MCP-X, an ultra-compact CNN with only 0.21 million parameters for real-time, on-device rice disease classification. MCP-X integrates a shallow encoder, multi-branch expert routing, a bi-level recurrent simulation encoder–decoder (BRSE), an efficient channel attention (ECA) module, and a lightweight classifier. Trained from scratch, MCP-X achieves 98.93% accuracy on PlantVillage and 96.59% on the Rice Disease Detection Dataset, without external pretraining. Mechanistically, expert routing diversifies feature branches, ECA enhances channel-wise signal relevance, and BRSE captures lesion-scale and texture cues—yielding complementary, stage-wise gains confirmed through ablation studies. Despite slightly higher FLOPs than MobileNetV2, MCP-X prioritizes a minimal memory footprint (~1.01 MB) and deployability over raw speed, running at 53.83 FPS (2.42 GFLOPs) on an RTX A5000. It achieves 16.7×, 287×, 420×, and 659× fewer parameters than MobileNetV2, ResNet152V2, ViT-Base, and VGG-16, respectively. When integrated into a multi-resolution ensemble, MCP-X attains 99.85% accuracy, demonstrating exceptional robustness across controlled and field datasets while maintaining efficiency for real-world agricultural applications. Full article

Rice sheath rot has progressively developed into a growing threat to global rice production, particularly in intensively managed systems conducive to disease development. Therefore, accurate identification of the causal pathogen and the development of sustainable management strategies represent urgent scientific requirements. In this study, we isolated the causal organism of rice sheath rot from infected rice tissues and identified it as Fusarium verticillioides based on multi-locus sequence analysis. Eight endophytic bacterial strains were recovered from healthy rice root systems. Among the isolates, Bacillus velezensis isolate 7-A exhibited the strongest antifungal activity against F. verticillioides. This isolate demonstrated broad-spectrum antifungal activity, with inhibition rates ranging from 54.8% to 71.8%. Phylogenetic analysis based on 16S rRNA and gyrB gene sequences identified it as B. velezensis. Further characterization revealed that B. velezensis 7-A is capable of secreting proteases and synthesizing siderophores. The filtered liquid from sterile fermentation markedly inhibited the growth of mycelium in F. verticillioides and induced marked morphological abnormalities. Liquid LC-MS analysis identified multiple antifungal active substances, including camphor, ginkgolides B, salicin, cinnamic acid, hydroxygenkwanin, stearamide, β-carotene, and others. A pot experiment demonstrated that the fermentation broth of B. velezensis 7-A effectively suppressed the occurrence of rice sheath rot, achieving a relative control efficacy of 61.3%, which is comparable to that of a 10% carbendazim water-dispersible granule (WDG). Additionally, isolate 7-A enhances plant disease resistance by activating the activities of key defense enzymes. These findings provide preliminary insights into its potential application in integrated and sustainable disease management programs. Full article