Search Results (180)

Magnaporthe oryzae-induced rice blast remains a critical threat to sustainable rice farming, causing extensive losses in many rice-producing regions worldwide. Due to increasing concerns about pesticide overuse and its impact on the environment and human health, alternative control methods are being actively explored. Nanotechnology has recently gained attention as a potential tool for sustainable disease management. This review summarises current progress in the use of nanomaterials—including metal and biopolymer nanoparticles, nanoemulsions, targeted delivery systems, and biosensors—for the detection and control of rice blast. Studies have reported that nanomaterials can reduce disease severity by up to 70% and improve rice yield by 10–20% under field or greenhouse conditions. The mode of action, effectiveness under field conditions, and possible integration into integrated pest management (IPM) programs are discussed. The selection of literature followed the PRISMA-P framework to ensure a systematic and transparent review process. Challenges such as biosafety, environmental risks, and regulatory issues are also addressed, with emphasis on green synthesis methods and the need for field validation before practical application. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

The yield and quality of rice are severely affected by rice disease, which can result in crop failure. Early and precise identification of rice plant diseases enables timely action, minimizing potential economic losses. Deep convolutional neural networks (CNNs) have significantly advanced image classification accuracy by leveraging powerful feature extraction capabilities, outperforming traditional machine learning methods. In this work, we propose a dual attention-guided lightweight network for fast and precise recognition of rice diseases with small lesions and high similarity. First, to efficiently extract features while reducing computational redundancy, we incorporate FasterNet using partial convolution (PC-Conv). Furthermore, to enhance the network’s ability to capture fine-grained lesion details, we introduce a dual-attention mechanism that aggregates long-range contextual information in both spatial and channel dimensions. Additionally, we construct a large-scale rice disease dataset, named RD-6, which contains 2196 images across six categories, to support model training and evaluation. Finally, the proposed rice disease detection method is evaluated on the RD-6 dataset, demonstrating its superior performance over other state-of-the-art methods, especially in terms of recognition efficiency. For instance, the method achieves an average accuracy of 99.9%, recall of 99.8%, precision of 100%, specificity of 100%, and F1-score of 99.9%. Additionally, the proposed method has only 3.6 M parameters, demonstrating higher efficiency without sacrificing accuracy. Full article

Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article

In rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and environment limitations, which affects the detection performance. In view of the fact that pests and diseases affect the whole situation and tiny details are mostly localized, we propose a rice image reconstruction method based on an adaptive two-branch heterogeneous structure. The method consists of a low-frequency branch (LFB) that recovers global features using orientation-aware extended receptive fields to capture streaky global features, such as pests and diseases, and a high-frequency branch (HFB) that enhances detail edges through an adaptive enhancement mechanism to boost the clarity of local detail regions. By introducing the dynamic weight fusion mechanism (CSDW) and lightweight gating network (LFFN), the problem of the unbalanced fusion of frequency information for rice images in traditional methods is solved. Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. In downstream task validation, rice panicle maturity detection achieves a 61.5% increase in mAP50 (0.480 → 0.775) compared to interpolation methods, and leaf pest detection shows a 2.7% improvement in average mAP50 (0.949 → 0.975). This research provides an effective solution for lightweight rice image enhancement, with its dual-branch collaborative mechanism and dynamic fusion strategy establishing a new paradigm in agricultural rice image processing. Full article

Traditional monitoring methods rely on manual field surveys, which are subjective, inefficient, and unable to meet the demand for large-scale, rapid monitoring. By using unmanned aerial vehicles (UAVs) to capture high-resolution images of rice canopy diseases and pests, combined with deep learning (DL) techniques, accurate and timely identification of diseases and pests can be achieved. We propose a method for identifying rice canopy diseases and pests using an improved YOLOv5 model (YOLOv5_DWMix). By incorporating deep separable convolutions, the MixConv module, attention mechanisms, and optimized loss functions into the YOLOv5 backbone, the model’s speed, feature extraction capability, and robustness are significantly enhanced. Additionally, to tackle the challenges posed by complex field environments and small datasets, image augmentation is employed to train the YOLOv5_DWMix model for the recognition of four common rice canopy diseases and pests. Results show that the improved YOLOv5 model achieves 95.6% average precision in detecting these diseases and pests, a 4.8% improvement over the original YOLOv5 model. The YOLOv5_DWMix model is effective and advanced in identifying rice diseases and pests, offering a solid foundation for large-scale, regional monitoring. Full article

Rice bakanae disease (RBD), a major threat in rice-cropping nations, can reduce rice yield and quality. As it is a seed-borne disease, effective seed detection is crucial. Loop-mediated isothermal amplification (LAMP) can rapidly and specifically amplify DNA at a constant temperature with high sensitivity. This research uses LAMP to develop rapid RBD pathogen detection systems. Primers were designed targeting the NRPS31 gene of Fusarium fujikuroi and conserved TEF1α sequences of Fusarium asiaticum, Fusarium proliferatum, and Fusarium andiyazi. These reactions at 60 °C for 60 min had a detection limit of 100 pg·μL⁻¹, and LAMP proved applicable in field trials. Full article

Diseases and weeds occupy a leading place among the factors limiting the yield of agricultural crops, including rice. These factors can be overcome through the use of chemical protective agents, as well as through the creation and introduction into agricultural production of rice varieties resistant to these stressors. The use of DNA marking technologies for target genes of economically valuable traits in the creation of promising varieties allows not only for the identification of genes but also the monitoring of their transmission during crosses and the selection of breeding-valuable genotypes with genes of interest. In addition, this ensures a reduction in the volume of breeding nurseries, as well as time and material costs during variety modeling, and rapid rotation of new high-yield varieties with specified characteristics. We have selected effective marker systems based on the use of DNA marking technologies for target genes for resistance to blast (Pi) and submergence tolerance (Sub1A). These systems allow for precise targeted selection of hybrid plants with these genes in the breeding process. In addition, we have automated the detection of transferred Pi-ta and Pi-b genes, which greatly relieves the DNA analysis during mass screening of breeding material. The final result of this work is the created rice varieties Al’yans, Lenaris and Kapitan with the Pi-ta blast resistance gene and the Pirouette rice variety with the Pi-1, Pi-2, and Pi-33 genes. These varieties exceed the standards by 0.64–2.2 t/ha, and their involvement in production makes it possible to obtain additional products by increasing yields in the amount of about RUB 80 thousand/ha. Full article

Rice (Oryza sativa) is a globally important crop that plays a central role in maintaining food security. This scientific review examines the critical role of genetic disease resistance in protecting rice yields, dissecting at the molecular level how rice plants detect and respond to pathogen attacks while evaluating modern approaches to developing improved resistant varieties. The analysis covers single-gene-mediated and multi-gene resistance systems, detailing how on one hand specific resistance proteins, defense signaling components, and clustered loci work together to provide comprehensive protection against a wide range of pathogens and yet their production is severely impacted by pathogens such as Xanthomonas oryzae (bacterial blight) and Magnaporthe oryzae (rice blast). The discussion extends to breakthrough breeding technologies currently revolutionizing rice improvement programs, including DNA marker-assisted selection for accelerating traditional breeding, gene conversion methods for introducing new resistance traits, and precision genome editing tools such as CRISPR/Cas9 for enabling targeted genetic modifications. By integrating advances in molecular biology and genomics, these approaches offer sustainable solutions to safeguard rice yields against evolving pathogens. Full article

Bacterial leaf streak (BLS) is one of the internationally significant quarantine diseases in rice. Effectively utilizing BLS resistance genes from wild rice (Oryza rufipogon Griff.) to breed new varieties offers a fundamental solution for BLS control. This study focused on the fine mapping of the BLS resistance gene bls2 and the development of closely linked molecular markers for breeding BLS-resistant lines. Using a Guangxi common wild rice accession DY19 (carrying bls2) as the donor parent and the highly BLS-susceptible indica rice variety 9311 as the recipient parent, BLS-resistant rice lines were developed through multiple generations of backcrossing and selfing, incorporating molecular marker-assisted selection (MAS), single nucleotide polymorphism(SNP) chip genotyping, pathogen inoculation assays, and agronomic trait evaluation. The results showed that bls2 was delimited to a 113 kb interval between the molecular markers ID2 and ID5 on chromosome 2, with both markers exhibiting over 98% accuracy in detecting bls2. Four stable new lines carrying the bls2 segment were obtained in the BC₅F₄ generation. These four lines showed highly significant differences in BLS resistance compared with 9311, demonstrating moderate resistance or higher with average lesion lengths ranging from 0.69 to 1.26 cm. Importantly, no significant differences were observed between these resistant lines and 9311 in key agronomic traits, including plant height, number of effective panicles, panicle length, seed setting rate, grain length, grain width, length-to-width ratio, and 1000-grain weight. Collectively, two molecular markers closely linked to bls2 were developed, which can be effectively applied in MAS, and four new lines with significantly enhanced resistance to BLS and excellent agronomic traits were obtained. These findings provide technical support and core germplasm resources for BLS resistance breeding. Full article

Rice blast disease is a major threat to rice yields. Sustainable control relies on resistant varieties, where plant immunity is triggered by pattern recognition receptors like receptor-like proteins (RLPs). The rice RLP chitin-elicitor binding protin (CEBiP) recognizes fungal chitin and confers blast resistance to pathogen Magnaporthe oryzae. However, understanding of the broader signaling and metabolomic pathways associated with CEBiP activation remains limited. Here, we performed an integrated transcriptomic and metabolomic analysis of the rice Zhonghua 11 genotype and CEBiP knockout plants. Both plants were infected with M. oryzae, and infected leaves were harvested at 24, 48, and 72 hpi for RNA sequencing and Liquid Chromatography-Tandem Mass Spectrometry analysis. Transcriptomics identified a total of 655 genes that were differentially regulated upon knockout of CEBiP; they were mainly related to diterpenoid/phenylpropanoid biosynthesis, nitrogen metabolism, the mitogen-activated protein kinasesignaling pathway, plant–pathogen interaction, and plant hormone signal transduction. The presence of a large number of pathogenesis-related protein 1 family genes indicates the key role of salicylic acid (SA) in CEBiP immunity. Metabolomics detected a total of 962 differentially accumulated metabolites and highlights the roles of caffeine and glutathione metabolism in CEBiP-mediated immunity. Since caffeine and glutathione metabolism can regulate SA signaling, we propose that SA signaling plays a central role in the CEBiP immune function. Full article

Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges to accurate recognition. To address these issues, this paper proposes a novel rice leaf disease detection model—RLDD-YOLOv11n. First, the improved RLDD-YOLOv11n integrates the SCSABlock residual attention module into the neck layer to enhance multi-semantic information fusion, thereby improving the detection capability for small disease targets. Second, recognizing the limitations of the native upsampling module in YOLOv11n in reconstructing rice-disease-related features, the CARAFE upsampling module is incorporated. Finally, a rice leaf disease dataset focusing on three common diseases—Bacterial Blight, Rice Blast, and Brown Spot—was constructed. The experimental results demonstrate the effectiveness of the proposed improvements. RLDD-YOLOv11n achieved a mean Average Precision (mAP) of 88.3%, representing a 2.8% improvement over the baseline model. Furthermore, compared with existing mainstream lightweight YOLO models, RLDD-YOLOv11n exhibits a superior detection performance and robustness. Full article

Introduction: Rice, a cornerstone of global food security, faces escalating demands for enhanced yield and disease resistance. We collected 300 high-quality genomes, representing both cultivated (Oryza sativa indica, O. sativa japonica, and O. sativa aus) and wild species (O. rufipogon, O. glaberrima, and O. barthii). Methods: Leveraging HMMER, NLR-Annotator, and OrthoFinder, we systematically identified 148,077 leucine-rich repeat (LRR) and 143,459 nucleotide-binding leucine-rich repeat (NLR) genes, with LRR receptor-like kinases (LRR-RLKs) dominating immune receptor proportions, followed by coiled-coil domain containing (CNL)-type NLRs and LRR receptor-like proteins (LRR-RLPs). Results: Benchmarking Universal Single-Copy Orthologs (BUSCO) assessments confirmed robust genome quality (average score: 94.78). Strikingly, 454 TIR-NB-LRR (TNL) genes—typically rare in monocots—were detected, challenging prior assumptions. Phylogenetic analysis with Arabidopsis TNLs highlighted five O. glaberrima genes clustering with dicot TNLs; these genes featured truncated PLN03210 motifs fused to nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4 (NB-ARC) and LRR domains. Conclusions: By bridging structural genomics, evolutionary dynamics, and domestication-driven adaptation, this work provides a foundation for targeted breeding strategies and advances functional studies of plant immunity in rice. Full article

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis. Full article

Crop diseases can significantly impact crop growth and production, often leading to a severe economic burden for rice farmers. These diseases can spread rapidly over large areas, making it challenging for farmers to detect and manage them effectively and promptly. Automated methods for disease classification emerge as promising approaches for detecting and managing these diseases, provided there are sufficient data. Sharing data among farms could facilitate the development of a strong classifier, but it must be executed properly to prevent leaking sensitive information. In this study, we demonstrate how farms with vastly different datasets can collaborate through a federated learning model. The objective of this collaboration is to create a classifier that every farm can use to detect and manage rice crop diseases by leveraging data sharing while safeguarding data privacy. We underscore the significance of data sharing and model architecture in developing a robust centralized classifier, which can effectively classify multiple diseases (and a healthy state) with 83.24% accuracy, 84.24% precision, 83.24% recall, and an 82.28% F1 score. In addition, we demonstrate the importance of model design on classification outcomes. The proposed collaborative learning method not only preserves data privacy but also offers a cost-effective and communication-efficient lightweight solution for rice crop disease detection. Furthermore, this collaborative strategy can be extended to other crop disease classification tasks. Full article