Search Results (79)

Morkhor 60-3 is an upland rice variety primarily cultivated in northeastern Thailand. This glutinous rice is valued for its adaptability and rich aroma but remains susceptible to significant diseases, particularly blast and bacterial blight. Using resistant varieties represents the most cost-effective approach to address this limitation. This study incorporated the QTLs/genetic markers qBl1, qBl2, and xa5 from Morkhor 60-1 through marker-assisted backcrossing. From the BC₁F₃ population, ten lines were selected based on their parentage and evaluated for blast resistance using a spray inoculation method with 12 isolates of Pyricularia oryzae, and for bacterial blight (BB) resistance using a leaf-clipping method with nine isolates of Xanthomonas oryzae pv. oryzae. Broad-spectrum resistance (BSR) was also assessed in the lines for both diseases. Subsequently, BC₁F₄ lines were evaluated for field performance, including agronomic traits and aroma. Results identified three superior lines, BC₁F₄ 22-7-140-4, BC₁F₄ 22-7-322-5, and BC₁F₄ 22-7-311-9, that demonstrated resistance to both BB and blast pathogens with average BSR values of 0.61 and 1.00, 0.66 and 1.00, and 0.55 and 0.87, respectively. These lines also exhibited enhanced performance in flowering date, plant height, panicle number per plant, grain number per plant, and grain weight. These findings demonstrate the effectiveness of marker-assisted selection (MAS) for gene pyramiding in rice improvement. 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

Difenoconazole, a broad-spectrum systemic fungicide, can effectively prevent and control plant diseases such as rice blast, leaf spot, and black spot caused by Colletotrichum godetiae, Alternaria alternata, and Neopestalotiopsis rosae. However, its residual accumulation in the environment may pose potential toxicity risks to non-target organisms. In this study, a highly efficient DIF-degrading microbial consortium TA01 was enriched from long-term pesticide-contaminated soil by a laboratory-based adaptive evolution strategy. The microbial consortium TA01 was able to degrade 83.87% of 50 mg/L of DIF within 3 days. In addition, three intermediate metabolites were identified using HPLC–MS/MS, and the results indicated that the degradation of DIF by microbial consortium TA01 may involve catalytic reactions such as hydrolysis, dehalogenation, and hydroxylation. High-throughput sequencing results showed that Pantoea, Serratia, Ochrobactrum, and Bacillus were the dominant microbial members involved in the degradation process. Finally, bioremediation capacity experiments showed that inoculation with microbial consortium TA01 was able to accelerate the degradation of DIF in the water–sediment system. The findings of this study not only enrich the microbial resources available for DIF degradation but also offer new potential strategies for in situ remediation of DIF contamination. Full article

Rice leaf diseases critically threaten global rice production by reducing crop yield and quality. Efficient disease detection in complex field environments remains a persistent challenge for sustainable agriculture. Existing deep learning-based methods for rice leaf disease detection struggle with inadequate sensitivity to subtle disease features, high computational complexity, and degraded accuracy under complex field conditions, such as background interference and fine-grained disease variations. To address these limitations, this research aims to develop a lightweight yet high-accuracy detection model tailored for complex field environments that balances computational efficiency with robust performance. We propose RDRM-YOLO, an enhanced YOLOv5-based network, integrating four key improvements: (i) a cross-stage partial network fusion module (Hor-BNFA) is integrated within the backbone network’s feature extraction stage to enhance the model’s ability to capture disease-specific features; (ii) a spatial depth conversion convolution (SPDConv) is introduced to expand the receptive field, enhancing the extraction of fine-grained features, particularly from small disease spots; (iii) SPDConv is also integrated into the neck network, where the standard convolution is replaced with a lightweight GsConv to increase the accuracy of disease localization, category prediction, and inference speed; and (iv) the WIoU Loss function is adopted in place of CIoU Loss to accelerate convergence and enhance detection accuracy. The model is trained and evaluated utilizing a comprehensive dataset of 5930 field-collected and augmented sample images comprising four prevalent rice leaf diseases: bacterial blight, leaf blast, brown spot, and tungro. Experimental results demonstrate that our proposed RDRM-YOLO model achieves state-of-the-art performance with a detection accuracy of 94.3%, and a recall of 89.6%. Furthermore, it achieves a mean Average Precision (mAP) of 93.5%, while maintaining a compact model size of merely 7.9 MB. Compared to Faster R-CNN, YOLOv6, YOLOv7, and YOLOv8 models, the RDRM-YOLO model demonstrates faster convergence and achieves the optimal result values in Precision, Recall, mAP, model size, and inference speed. This work provides a practical solution for real-time rice disease monitoring in agricultural fields, offering a very effective balance between model simplicity and detection performance. The proposed enhancements are readily adaptable to other crop disease detection tasks, thereby contributing to the advancement of precision agriculture technologies. Full article

In order to analyze the physiological regulation mechanisms associated with exogenous melatonin on rice blast, this study treated rice seedlings with different concentrations of melatonin (0, 20, 100, and 500 µmol/L) in order to investigate the growth characteristics, root morphology, superoxide dismutase (SOD) activity, peroxidase (POD) activity, catalase (CAT) activity, malondialdehyde (MDA) content, hydrogen peroxide (H₂O₂) content, and soluble protein content of rice seedlings. The results indicated that 100 µmol/L of melatonin exhibited a significant effect, improving the growth and antioxidant capacity of rice seedlings under rice blast fungus infection. The disease resistance level of rice seedlings against rice blast significantly decreased by 31.58% when compared to the 0 µmol/L melatonin treatment, while the plant height, stem base width, plant leaf area, total root length, aboveground dry weight, aboveground fresh weight, and underground fresh weight significantly increased by 8.72% to 91.38%. Treatment with 100 µmol/L of melatonin significantly increased catalase activities and soluble protein content, with respective increases of 94.99% and 31.14%. Simultaneously, the contents of malondialdehyde and hydrogen peroxide significantly decreased, reaching 18.65% and 38.87%, respectively. The gray relational grade analysis indicated that hydrogen peroxide content and resistance level exhibit the highest gray relational grades with melatonin concentration and, so, can be used to evaluate the effect of melatonin on the severity of rice blast fungus infection. Furthermore, the membership function analysis revealed that the 100 µmol/L melatonin treatment had the highest membership function value, indicating a significant improvement in the resistance of rice seedlings to rice blast disease. In conclusion, 100 µmol/L of melatonin enhances the resistance of rice seedlings to rice blast disease through promoting their growth and strengthening their antioxidant defenses. This study provides new insights into the tolerance mechanisms of rice seedlings against rice blast disease. Full article

During vegetative growth, plants undergo various morphological and physiological changes in the transition from the juvenile phase to the adult phase. In terms of stress resistance, it has been suggested that plants gain or reinforce disease resistance during the process of maturation, which is recognized as adult plant resistance or age-related resistance. While much knowledge has been obtained about changes in disease resistance as growth stages progress, knowledge about changes in plant responses to pathogens with progressing age in plants is limited. In this study, we experimentally compared rice blast resistance in rice leaves sampled from plants at different growth phases. The results indicate differential infection progression and fungal status depending on growth stage. Transcriptome analysis following blast fungus infection revealed that several genes involved in the defense response were upregulated in both the juvenile and intermediate stage, but the expression changes of many genes were growth phase-specific. These findings highlight differences in rice leaf stress responses to blast infection at different growth stages. Full article

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI_RB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI_RB was designed. GRVI_RB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI_RB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. Full article

This study aimed to develop and genetically characterize thermosensitive genic male-sterility (TGMS) lines for use in hybrid rice (Oryza sativa L.) breeding. Male-sterile F₂ to F₄ generation lines were screened during the high-temperature summer season, and ratoon crops of selected male-sterile rows were harvested for pure seed. Sixty-six F₅ TGMS lines were genotyped using DNA markers controlling 16 traits from the LSU80 QA/QC Rice PlexSeq SNP Panel. Ten TGMS lines with desirable traits that included semidwarf, glabrous, non-aromatic, long-grain, narrow brown leaf spot resistance, and blast resistance genes were selected for further genotypic characterization using markers for low chalkiness (chalk5), wide compatibility (S5-n), cold tolerance (qSCT-11 and qCST-12), and anaerobic germination (AG1 and AG2). TGMS lines TIL21051S and TIL21052S possess favorable alleles for each of the genes evaluated in this study and are desirable parents for two-line hybrid breeding in the southeast United States. TIL21044S, TIL21095S, TIL21060S, and TIL21066S each contain three blast resistance genes and have potential as parental lines. TIL21014S-2, TIL21015S, and TIL21016S-1 include the fgr allele for aroma and can also be used as parental lines for aromatic two-line hybrids. Full article

The rice variety Bomba is grown in Calasparra—a rice of origin in southeast Spain—resulting in a product with excellent cooking quality, although its profitability has declined in recent years due to low grain yields and susceptibility to rice blast disease (Pyricularia oryzae Cavara). An innovation project to test the efficacy of mechanized transplanting against traditional direct seed sowing was conducted in 2022 and 2023 at four locations for the first time. A lower plant density (67–82 plants m⁻²) and shorter plants with higher leaf nitrogen content were observed in transplanted plots compared with seed sowing (130–137 plants m⁻²) in the first year. The optimal climatic conditions for P. oryzae symptom appearance were determined as temperatures of 25–29 °C and a 50–77% relative humidity. The most-affected sowing plots presented 3–20% leaf area damage and a reduction in yield to values of 1.5 t ha⁻¹ in the first year and 2.12 t ha⁻¹ in the second year. In transplanted plots, there was generally less humidity at the plant level and therefore, disease incidence was low in both seasons. Grain yields did not significantly differ among the treatments studied; however, there were differences in the yield components of panicle density and the number of grains for panicles. Principal component analysis revealed two principal components that explained 81% of the variability. Variables related to yield contributed positively to the first component, while plant biomass variables contributed to the second component. Plant density, tiller density, and panicle density were found to be positively correlated (r > 0.81 ***). Overall, transplanting (frame of 30 × 15–18 cm²) resulted in uniform crop growth with less rice blast disease, as well as higher grain yields (2.92–3.89 t ha⁻¹), in comparison with the average for the whole D.O. Calasparra (2.3–2.5 t ha⁻¹) in both seasons and a good percentage of whole grains at milling. This is novel knowledge which can be considered useful for farmers operating in the region. Full article

Rice is the primary staple food in many Asian countries, and ensuring the quality of rice crops is vital for food security. Effective crop management depends on the early and precise detection of common rice diseases such as bacterial blight, blast, brown spot, and tungro. This work presents a convolutional neural network model for classifying rice leaf disease. Four distinct diseases, bacterial blight, blast, brown spot, and tungro, are the main targets of the model. Previously, leaf pathologies in crops were mostly identified manually using specialized equipment, which was time-consuming and inefficient. This study offers a remedy for accurately diagnosing and classifying rice leaf diseases through deep learning techniques. Using this dataset, the proposed CNN model was trained to identify complex patterns and attributes linked to each disease using its deep learning capabilities. This CNN model achieved an exceptional accuracy of 99.99%, surpassing the benchmarks set by existing state-of-the-art models. The proposed model can be a useful diagnostic and early warning system for rice leaf diseases. It could help farmers and other agricultural professionals reduce crop losses and enhance the quality of their yields. Full article

Programmed cell death (PCD) plays critical roles in plant immunity but must be regulated to prevent excessive damage. In this study, a novel spotted leaf (spl11-1) mutant was identified from an ethyl methane sulfonate (EMS) population. The SPL11-1 gene was genetically mapped to chromosome 12 between the Indel12-37 and Indel12-39 molecular markers, which harbor a genomic region of 27 kb. Annotation of the SPL11-1 genomic region revealed the presence of two candidate genes. Through gene prediction and cDNA sequencing, it was confirmed that the target gene in the spl11-1 mutant is allelic to the rice SPOTTED LEAF (SPL11), hereafter referred to as spl11-1. Sequence analysis of SPL11 revealed a single bp deletion (T) between the spl11-1 mutant and the ‘Shuangkang77009’ wild type. Moreover, protein structure analysis showed that the structural differences between the SPL11-1 and SPL11 proteins might lead to a change in the function of the SPL11 protein. Compared to the ‘Shuangkang77009’ wild type, the spl11-1 mutant showed more disease resistance. The agronomical evaluation showed that the spl11-1 mutant showed more adverse traits. Through further mutagenesis treatment, we obtained the spl11-2 mutant allelic to spl11-1, which has excellent agronomic traits and more improvement and may have certain breeding prospects in future breeding for disease resistance. Full article

Rice blast is one of the most devastating biotic stresses that limits rice productivity. The North Eastern Hill (NEH) region of India is considered to be one of the primary centres of diversity for both rice and pathotypes of Magnaporthe grisea. Therefore, the present study was carried out to elucidate the genetic basis of leaf and neck blast resistance under Meghalaya conditions. A set of 80 diverse genotypes (natural population) and 2 F₂ populations involving resistant parent, a wildtype landrace, LR 5 (Lal Jangali) and susceptible genotypes Sambha Mahsuri SUB 1 (SMS) and LR 26 (Chakhao Poireiton) were used for association analysis of reported major gene-linked markers with leaf and neck blast resistance to identify major effective genes under local conditions. Genotyping using twenty-five gene-specific markers across diverse genotypes and F₂ progenies revealed genes Pi5 and Pi54 to be associated with leaf blast resistance in all three populations. Genes Pib and qPbm showed an association with neck blast resistance in both natural and LR 5 × SMS populations. Additionally, a set of 184 genome-wide polymorphic markers (SSRs and SNPs), when applied to F₂-resistant and F₂-susceptible DNA bulks derived from LR 5 × LR 26, suggested that Pi20(t) on chromosome 12 is one of the major genes imparting disease resistance. Markers snpOS318, RM1337 and RM7102 and RM247 and snpOS316 were associated with leaf blast and neck blast resistance, respectively. The genotypes, markers and genes will help in marker-assisted selection and development of varieties with durable resistance. Full article

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient ($Rc{v}^2$) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. Full article

RD6, the most popular glutinous rice in Thailand, is high in quality but susceptible to blast and bacterial blight disease. It was thus improved for disease resistance through marker-assisted backcross selection (MAS). The objective of this study was to evaluate the performance of improved near isogenic lines. Eight RD6 rice near isogenic lines (NILs) derived from MAS were selected for evaluation with RD6, a standard susceptible check variety, as well as recurrent parent for a total of nine genotypes. The experiment was conducted during the wet season under six environments at three locations, Khon Kaen, Nong Khai, and Roi Et, which was repeated at two years from 2019 to 2020. Nine genotypes, including eight RD6 rice near isogenic lines (NILs) selected from two in-tandem breeding programs and the standard check variety RD6, were evaluated to select the high-performance new improved lines. The first group, including four NILs G1–G4, was gene pyramiding of blast and BB resistance genes, and the second group, including another four NILs G5–G8, was gene pyramiding of blast resistance and salt tolerance genes. Field disease screening was observed for all environments. Two disease occurrences, blast (leaf blast) and bacterial blight, were found during the rainy season of all environments. The NILs containing blast resistance genes were excellent in gene expression. On the other hand, the improved lines containing the xa5 gene were not highly resistant under the severe stress of bacterial blight (Nong Khai 2020). Notwithstanding, G2 was greater among the NILs for yield maintenance than the other genotypes. The agronomic traits of most NILs were the same as RD6. Interestingly, the traits of G2 were different in plant type from RD6, specifically photosensitivity and plant height. Promising rice RD6 NILs with high yield stability, good agronomic traits, and disease resistance were identified in the genotypes G1, G2, and G7. The high yield stability G1 and G7 are recommended for widespread use in rain-fed areas. The G2 is specifically recommended for use in the bacterial blight (BB) disease prone areas. Full article

Through backcrossing and marker-assisted selection, gene Pi9 for resistance to rice blast was introduced into the water-saving and drought-resistant rice variety, Hanhui 3. The genetic background identity between Hanhui 8200 and Hanhui 3 was 91.4%. The drought resistance and drought avoidance abilities of Hanhui 8200 were equivalent to those of Hanhui 3. The resistance to rice blast was improved from grade 7 to grade 1. The rice quality of Hanhui 8200 meets the Ministry of Agriculture’s grade 3 rice standards. The two-line and three-line hybrids formulated with Hanhui 8200 have high yield potential. Among them, the three-line hybrid Hanyou 8200 (Approval No.: Evaluated Rice 20210073), formulated with Huhan 7A, passed the Hubei Provincial approval in 2021, and the two-line hybrid Hanyouliangyou 8200 (Approval No.: Nationally Validated Rice 20210448), formulated with Huhan 82S, passed the national variety approval in 2021. Both hybrids demonstrated strong resistance to rice blast, moderate resistance to bacterial leaf blight, strong drought resistance, high quality, and high yield. Full article