The invention relates to the field of crop breeding, in particular to a rice blast detection and counting method, which adopts the technical scheme that by improving a YOLOv8 detection algorithm and introducing a C2f-Aster module, a BFPN module, a WAdapt-IoU loss function and an LSCSAD detection head, the model performance is optimized, the parameter quantity and the calculation complexity are reduced, the characteristic expression ability to rice blast scabs and diseased leaves is enhanced, and the rice blast detection accuracy is improved. The method is suitable for resource-limited rice blast detection scenes, the detection capability of the model on rice blast disease spots and diseased leaves is improved, the generalization capability of the rice blast detection model is enhanced, the rice blast disease spot boundary detection precision is enhanced, the positioning precision of the disease spots and the diseased leaves is improved, and efficient and accurate rice blast detection and counting are achieved. And the accuracy and efficiency of rice blast disease detection and counting are improved.
This invention provides the application of FSD2 in regulating resistance to rice sheath blight and bacterial leaf blight, belonging to the field of gene breeding technology. This invention provides... FSD2 The application of genes and / or encoded proteins in regulating resistance in rice to infectious diseases caused by pathogenic microorganisms, the aforementioned FSD2 Genes are involved in regulating rice resistance to sheath blight and bacterial leaf blight. This invention obtains these genes through genetic transformation. FSD2 Overexpressing plants, compared with wild-type plants, showed that overexpressing plants... FSD2 The gene expression level was significantly higher than that of the wild type; after inoculation with *Rhizoctonia solani* and *Bacterium oxysporum*, the overexpressing plants showed significantly enhanced resistance to *Rhizoctonia solani* and *Bacterium oxysporum*. This invention provides a theoretical basis for elucidating the disease-resistant properties of plants, and can also be applied to the screening and creation of disease-resistant rice materials, laying a germplasm foundation for future variety improvement.
The application belongs to the technical field of plantdisease prevention and treatment, and provides application of tetrahydro-gamma-carboline derivative. The structural formula of the tetrahydro-gamma-carboline derivative is: The derivative has a good inhibitory effect on fungal diseases of rice and tobacco, and can be used for preparing pesticides for preventing and treating tobacco target spot disease, tobacco brown spotdisease, rice sheath blight disease and rice blast disease, and the application provides a new parent structure for development of the pesticides. The two kinds of tetrahydro-gamma-carboline derivatives provided by the application can be compounded with various types of pesticides, have a good synergistic effect on the above plant diseases, and can be used for delaying resistance development of existing commercial pesticides and prolonging the life cycle.
The invention provides a rice blast condition dynamic monitoring method based on a novel vegetation index and an unmanned aerial vehicle time sequencemultispectral image. The method comprises the following steps: obtaining canopy spectrum and condition data of health and different condition severity susceptible samples in a plurality of phenological stages; constructing a multispectral rice blast vegetation index based on the canopy spectrum and the disease index, and constructing a disease index estimation model based on the multispectral rice blast vegetation index; and drawing a multi-temporal disease index spatial distribution map based on the time sequence unmanned aerial vehicle multispectral image, and drawing a disease index daily growth rate spatial distribution map based on the multi-temporal disease index spatial distribution map. According to the method, the disease index estimation precision is remarkably improved by constructing the novel vegetation index, and the defect that traditional static investigation is difficult to reflect the disease development trend is overcome through the time sequence dynamic index. The novel rice blast vegetation index of the method can be suitable for most of multispectral images, the illness condition drawing and sequence analysis steps are simple, the calculation cost is low, and the method can be widely applied to illness condition investigation, accurate pesticide application and pesticide effect tracking.
This invention belongs to the field of rice blast disease control technology, specifically relating to a polypeptide MP1 that inhibits rice blast fungus and its application. This invention discloses a polypeptide MP1 that inhibits rice blast fungus, the amino acid sequence of which is shown in SEQ ID NO.1. This polypeptide MP1 has a significant inhibitory effect on rice blast fungus appressoriums; after 24 h, the MP1 polypeptide significantly inhibits the formation of ECs on appressoriums. 50 The concentration was 134.60 μM. Experiments showed that the peptide could effectively interfere with the normal differentiation and development of appressorium, a key stage in the pathogenesis of rice blast fungus, thus exerting strong control potential in the early stages of pathogen infection. The peptide MP1 provided by this invention can significantly reduce the pathogenicity of rice blast fungus to barley and rice, providing experimental evidence for the development of novel biological pesticides for the control of rice blast.
The present application relates to the technical field of agricultural disease detection and cropdisease resistance screening, and in particular to a rice bacterial leaf blightdisease resistance screening method based on a UAV and deep learning, which has the technical scheme of: an optimized YOLOv11-OBB model is composed of a C3k2FC module, an SPPF_LSKA module, a SlimNeck module and a LiteHead module, is responsible for extracting features in an image and locating bacterial leaf blight spots, and can efficiently and accurately detect bacterial leaf blight spots; a lightweight deep learning model is used, can efficiently run on a resource-limited UAV platform, and reduces device and computing costs; through cooperation of multiple optimization modules, the detection accuracy of bacterial leaf blight spots is significantly improved, and stable detection effects can be maintained; the operation process is simplified, and full automation of bacterial leaf blight disease resistance screening is realized; therefore, by using the lightweight and optimized YOLOv11-OBB model and combining the module design with high computing efficiency, low-cost, efficient, automated and high-precision bacterial leaf blight disease resistance screening is realized, and the method is suitable for large-scale field application.
This invention relates to the field of pesticide technology, specifically to a soluble concentrate containing kasugamycin and its preparation process. By mass percentage, the raw materials for preparation include at least 0.1-8% kasugamycin, 0.1-30% plant growth regulators, 1-8% surfactants, 0.1-2% pH adjusters, 5-40% stabilizers, and deionized water to make up the balance. Specifically, this invention uses a combination of orthosilicic acid and kasugamycin. By controlling the amount of orthosilicic acid added to the soluble concentrate to 1-3 wt%, the mass ratio of kasugamycin to orthosilicic acid is (2-4):(1-3). This ensures product stability while achieving effective control of rice blast disease, exhibiting control effects no less than conventional agents such as tricyclazole and isoprothiolane, and with relatively lower pesticide residues compared to these agents, effectively improving rice quality and ensuring the safety of agricultural products.
The invention provides a molecular identificationchip aiming at permanent resistance of rice blast and application thereof, the chip comprises 600-800 specific nucleic acid probes fixed on a solid phase carrier, each probe corresponds to a functional genetic locus subjected to multi-year, multi-point and multi-race inoculation tests and whole genome association analysisverification, and the molecular identificationchip is used for identifying the permanent resistance of the rice blast. According to the method, a comprehensive resistance index (CRI) prediction model is constructed by combining high-throughputtyping with machine learning. During breeding, the lasting resistance level of the material to multiple rice blast can be rapidly quantified in the seedling stage, and early-stage accurate screening is achieved. Compared with a traditional method depending on a single major R gene, the method has the advantages that the resistance broad spectrum and durability are remarkably improved, the field identification cost is reduced by 70% or above, and pesticide application reduction and green production are supported. The technology provides an efficient, intelligent and sustainable molecular tool for rice disease-resistant breeding.
The application discloses application of a compound BTD-81 in preparation of a medicine for preventing and treating Pyricularia oryzae, and belongs to the field of plantfungal disease prevention and treatment. The compound BTD-81 has a remarkable effect in preventing and treating rice blast or / and other pathogenic fungi, can effectively inhibit sporepathogenicity of the Pyricularia oryzae in a concentration range of 10-50 ppm, and especially, the compound BTD-81 at 50 ppm can completely prevent and treat the rice blast. The compound BTD-81 can be used as a protective agent, has a better effect when used at an initial stage of the rice blast infection, and under the condition of spraying the compound, the growth state of the rice is good, and there is no obvious adverse effect, indicating that the compound is safe to the rice, and has a practical significance for preventing the rice blast disease.