4973 results about "Oryza sativa" patented technology

The present application relates to a kind of deletion mutant nucleic acid and its application in anti-herbicide.The deletion mutation occurs in the promoter of rice OsHPPD Gene, the length of deletion sequence is at least 10 bp, and the A in the start codon ATG of gene is 0, at least the bases located in the upstream of start codon ATG from 2041 to 2032 are deleted. OsHPPD The present application finds that by deleting part of the sequence in the promoter of rice OsHPPD Gene, rice can obtain resistance to HPPD inhibitor herbicides, which is of great application value for ensuring agricultural production safety and improving the efficiency of herbicide use.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention belongs to the technical field of microorganisms, particularly relates to rice endophytic bacillus velezensis AN6 and application thereof, and particularly provides bacillus velezensis AN6 which is classified and named as bacillus velezensis and has the preservation number of CCTCC (China Center for Type Culture Collection) NO: M 20231886. The invention provides a novel rice endophytic bacillus velezensis strain AN6, through separation, screening, identification and experimental verification, it is found that the strain shows broad-spectrum resistance to various phytopathogens including rice bacterial leaf blight, rice blast and the like, the strain has the capacity of inhibiting various phytopathogens and has the capacity of adsorbing heavy metal, and the strain has the advantages of being capable of inhibiting various phytopathogens and capable of being used as a novel rice endophytic bacillus velezensis strain AN6. The method is of great significance to biological control of rice diseases and development of green agriculture.

The invention relates to the field of rice gene engineering, and particularly provides OsSULTR2; the invention also discloses application of the 2 protein and the coding gene thereof in regulating and controlling the salt tolerance of rice. The protein meets the following conditions: B1) a protein with an amino acid sequence of SEQ ID NO.1; and B2) a fusion protein with the same function obtained by connecting a tag to the N end and / or C end of B1). The method is used for detecting OsSULTR2; the salt stress phenotype identification in the seedling stage is carried out on the transgenic rice with the gene knockout 1, 2, and the result shows that when the gene segment is deleted, the salt stress tolerance of the rice is improved, and the function and the application way of the gene are proved. Therefore, the OsSULTR2 of the present invention; the 2 protein and the coding gene thereof can regulate and control the salt tolerance of rice, and have important significance for cultivating salt-tolerant transgenic rice.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention relates to a deleted mutant nucleic acid and application thereof in herbicide resistance. The deletion mutation occurs in a promoter of a rice OsHPPD gene, the length of a sequence of a deleted nucleic acid is at least 25 bp, A in an initiation codon ATG of the OsHPPD gene is taken as a 0 site, and at least bases at 2299 to 2275 sites on the upstream of the initiation codon ATG are deleted. According to the application disclosed by the invention, the rice can obtain resistance to HPPD inhibitor herbicides by deleting part of sequences in the promoter of the rice OsHPPD gene, so that the application has great application values in guaranteeing agricultural production safety and improving the use efficiency of the herbicides.

The invention discloses a lightweight rice leaf disease identification method based on YOLO target detection. The method comprises the following steps: S1, inputting a rice leaf disease image; s2, carrying out super-resolution reconstruction on the image by using an improved Real-ESRGAN algorithm; s3, inputting the picture into an improved YOLOv8n target detection algorithm; s4, obtaining disease spot category and position information; and S5, visualizing the information on the image. The invention relates to the technical field of target detection, and has the beneficial effects that image super-resolution reconstruction is carried out aiming at the problems that a rice leaf scab target is relatively small and an image acquired in real time is relatively fuzzy, so that the resolution of the small target is improved, and the definition and texture features are improved. On the basis of a super-division model Real-ESRGAN, a group of residual dense modules containing five layers of cavity convolution layers are designed to help the network to acquire receptive fields and information of different scales.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention relates to a deleted mutant nucleic acid and application thereof in herbicide resistance. The deletion mutation occurs in a promoter of the rice OsHPPD gene, the length of a deletion sequence is at least 10 bp, A in an initiation codon ATG of the OsHPPD gene is taken as a 0 site, and at least 2041-2032 sites of basic groups located on the upstream of the initiation codon ATG are deleted. According to the application disclosed by the invention, the rice can obtain resistance to HPPD inhibitor herbicides by deleting part of sequences in the promoter of the rice OsHPPD gene, so that the application has great application values in guaranteeing agricultural production safety and improving the use efficiency of the herbicides.

The invention discloses a rice leaf tip small target detection and counting method based on global information enhancement, and the method comprises the steps: firstly building a field rice leaf tip image data set as a basis, and constructing an improved YOLO-DCL model; the core innovation of the method lies in that a global context information enhancement module, an inverted residual-cascade grouping attention module and a re-parameterization shared convolution detection head are introduced, key feature expression is enhanced and detection efficiency is optimized through effective fusion of global information, the recognition precision and processing speed of the model on a tiny rice leaf tip target are significantly improved, and the method is suitable for large-scale popularization and application. After training parameters are set, an optimization model is trained, then system performance verification and module optimization are carried out on the model, and a version with the optimal rice leaf tip detection performance is screened out. The technical bottlenecks that small target leaf tips are prone to missing detection and false detection and insufficient in real-time performance under the complex farmland background are effectively overcome, and reliable and efficient technical support is provided for rice growth early-stage yield prediction.

The invention discloses a rice straw decomposition process full life cycle monitoring system based on a cloud platform, and relates to the field of agricultural informatization, and the system comprises a field data collection terminal which is used for collecting near infrared spectrum data, image data and operation and environment data of a straw decomposition process; the layered identification and modeling unit is used for identifying initial mass proportions and decomposition progress of soluble components, cellulose components, hemicellulose components and lignin components of the straws, and establishing a layered progress bar; the fusion progress calculation unit is used for weighting each layered progress bar based on the initial mass ratio to generate an overall decomposition progress bar; the progress output unit is used for simultaneously outputting a layered progress bar and an overall decomposition progress bar; and the mode mining unit is used for clustering the historical layered progress bars, forming a progress category prototype library and outputting potential blocking reasons. The problems that the field straw decomposition process is difficult to monitor in real time and the blocking reason is difficult to identify are solved.

The invention relates to the technical field of rice grain detection, in particular to a rice grain mixed-planting rapid nondestructive detection method based on hyperspectral imaging, which comprises the following steps: S1, collecting a rice hundred-grain board hyperspectral image, carrying out preprocessing and region-of-interest segmentation, and extracting a full-wave band spectrum grayscale image of a target region; s2, constructing a multi-channel attention mechanism network model suitable for nondestructive testing of mixed rice grains, and inputting the full-wave band spectrum grayscale image as a data set for classification training; according to the rapid nondestructive testing method for mixed planting of the rice grains based on hyperspectral imaging, spectrum and spatial characteristics of the rice grains are fused, spectrum and spatial characteristic information of the rice grains is enhanced through a multi-channel attention mechanism and a self-attention mechanism module, the classification precision of the rice grains is further helped to be improved, and the detection accuracy of the mixed planting of the rice grains is improved. According to the multi-channel attention mechanism network model provided by the invention, a segmentation module and a classification module are fused, so that the parameter quantity during high-resolution image training can be reduced.

The invention relates to a rice identification method based on optical and SAR image fusion, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: synchronously acquiring SAR and optical time sequence data, respectively preprocessing the SAR and optical time sequence data, and extracting characteristic parameters; the method comprises the following steps: constructing a double-flow Vision Transform model, wherein the double-flow Vision Transform model comprises a double-branch feature extraction module, a space-time Transform aggregation module, a gating feature fusion module and a decoder; the double-branch feature extraction module extracts SAR and optical features according to the SAR and optical feature parameters; inputting the extracted SAR features and optical features into a space-time Transform aggregation module, and carrying out time sequence dependence modeling through a self-attention mechanism; the gating feature fusion module performs weighted fusion on the optical and SAR features; and the decoder generates a rice distribution probability graph according to a weighted fusion result. According to the method, the rice identification precision under the multi-temporal and multi-environment conditions can be improved.

The invention belongs to the technical field of plant genetic engineering, and particularly relates to an SCRE10 gene for improving rice disease resistance and application of the SCRE10 gene, the base sequence of the SCRE10 gene is shown as SEQ ID NO.1, and the amino acid sequence of the SCRE10 gene is shown as SEQ ID NO.2. Through construction of a dexamethasone induced expression SCRE10 transgenic rice plant, it is found that the transgenic rice can significantly induce PR gene expression and active oxygen outbreak, and the resistance of rice to false smut and bacterial leaf blight can be improved. The invention proves that heterologous inducible expression of the Ustilaginoidea virens SCRE10 gene has the function of positively regulating the disease resistance of the rice, and the SCRE10 gene can be used for improving the disease resistance of the rice, which is of great significance to the creation of disease-resistant germplasm of the rice.

The invention discloses a lightweight rice pest detection method based on GAFNet and electronic equipment. A global attention fusion and spatial pyramid pooling module is designed to capture a global context and aggregate multi-scale features. On this basis, a C3-EFSA is provided, feature representation is optimized through depth separable convolution and a lightweight channel attention mechanism, and therefore the distinguishing ability under the complex background is improved. An enhanced Ghost detection head is constructed, and enhanced Ghost convolution (EGConv), an SE module and a SiLU activation function are integrated, so that redundancy is reduced, and a lightweight structure is further improved. And finally, an enhanced loss function FECIoU specially optimized for a complex pest detection scene is provided, the function is based on CIoU, a numerical stable term and a difficult sample weighting mechanism are introduced, and the positioning robustness of the shielded pests is further optimized. The lightweight target detection model can realize detection of different types of rice insect pests, and is suitable for actual deployment.

The invention discloses a multi-platform real-time field rice ear detection and counting method based on a visual converter, which is suitable for various acquisition platforms from a portable mobile terminal to an unmanned aerial vehicle terminal and the like, and meets the requirements of small-range accurate identification and large-range high-throughput monitoring. Aiming at the characteristics of multiple scales, multiple varieties, phenotype difference, time sequence change, complex field environment and the like in a data set, a rice spike detection counting network comprising a feature enhancement module, an efficient cross-space feature extraction network, a lightweight bidirectional feature fusion module and a number estimation and detection head module is constructed. The average precision mean value (mAP50) of the model reaches 0.8906 and is improved by 11.5% compared with that of an original model, the parameter quantity is reduced by 29%, and the calculated quantity (GFLOPs) is reduced by 48%. The method can effectively process rice ear images of different scales and growth periods, adapts to morphological change, overcomes interference such as illumination, phenotypic difference and shielding, and provides technical support for rice yield evaluation and field rice ear quality analysis.

The invention discloses a method and a system for predicting diseases and insect pests of saline-alkaline tolerant rice based on artificial intelligence. The method comprises the steps of collecting rice data, optimizing the rice data, establishing a prediction model for the diseases and insect pests of the saline-alkaline tolerant rice and predicting the diseases and insect pests of the saline-alkaline tolerant rice. The invention belongs to the technical field of agricultural information, and particularly relates to a method and a system for predicting diseases and insect pests of saline-alkali tolerant rice based on artificial intelligence. According to the scheme, rhombic plot segmentation is dynamically adjusted according to the irregular terrain and the saline-alkali accumulation area boundary of the saline-alkali tolerant rice field, and the collaborative weight is calculated based on the saline-alkali stress degree and the disease and insect pest risk level; key region learning is enhanced in combination with a plot total score and a selection probability formula; a plot score, a neighborhood pest and disease density mean value and a neighborhood saline-alkaline gradient are introduced into a spatial branch, so that the capturing capability of spatial correlation is improved; on the basis of the dynamic adjustment tolerance threshold, the influence of the extreme value is limited through the loss upper limit coefficient, a pest and disease prediction error regulation and control function is constructed, and the pest and disease prediction accuracy is improved.

The invention discloses a compound microbial agent for reducing the cadmium content of brown rice and application of the compound microbial agent. Belongs to the technical field of agricultural microorganisms. The compound microbial agent is prepared from herbaceous spirillum, stenotrophomonas and coxakia, the herbaceous spirillum is herbaceous spirillum R3, and the preservation number of the herbaceous spirillum is CCTCC (China Center For Type Culture Collection) NO: M 20231757; the stenotrophomonas is stenotrophomonas R5, and the preservation number of the stenotrophomonas is CCTCC (China Center For Type Culture Collection) NO: M 20231758; the coxakia is coxakia P1-4, and the preservation number of the coxakia is CCTCC (China Center For Type Culture Collection) NO: M 20231759. Compared with the prior art, the compound microbial agent disclosed by the invention has the beneficial effects that the compound microbial agent disclosed by the invention can reduce the content of cadmium in brown rice, so that farmland soil polluted by light and medium cadmium can be repaired and produced at the same time, and the edible safety of agricultural product rice is ensured.

The invention provides a method for preparing carbon quantum dot reinforced subcritical artificial humic acid and applying the carbon quantum dot reinforced subcritical artificial humic acid to a rice field, which comprises the following steps: collecting organic wastes, and drying, grinding and sieving the organic wastes to obtain pretreated raw materials; the preparation method comprises the following steps: mixing pretreated raw materials with water, reacting in a hydrothermal carbonization system at 200-280 DEG C for 1-3 hours, modifying through acid-base pretreatment, adjusting a reaction medium or an additive, and naturally cooling to obtain a natural substance subjected to hydrothermal carbonization; carrying out solid-liquid separation on the natural substance subjected to hydrothermal carbonization by using a 100-mesh coarse filter screen to obtain solid-phase hydrothermal carbon and liquid-phase artificial humic acid; carrying out purification treatment on the liquid-phase artificial humic acid for 48 hours by using a dialysis bag to obtain carbon quantum dots and characterizing the carbon quantum dots; the carbon quantum dot reinforced subcritical artificial humic acid is applied to a rice field, and is additionally applied in a fertilization period. Compared with a traditional composting technology, the scheme has the advantages that the carbon conversion rate is increased by 2-3 times, the soil fertility can be improved, and the rice yield is increased.

The invention discloses a multi-modal fusion identification method and system for rice leaf type diseases and insect pests. The method comprises the following steps: respectively carrying out feature extraction on image data and non-imaging multispectral data of a detected rice area to obtain image features and spectral features; performing cross attention feature extraction on the image features and the spectral features, and performing adaptive gating feature fusion on the obtained features to obtain modal fusion features; according to the gating feature fusion structure provided by the invention, the fusion weight is dynamically adjusted according to different input features so as to improve the flexibility of model information fusion, so that the fusion features containing different modal effective information are obtained, and the accuracy of rice leaf type disease and pest identification is improved. In addition, the spectral reflectivity data and the spectral vegetation index are subjected to feature extraction at the same time, feature encoders corresponding to the spectral reflectivity data and the spectral vegetation index are stacked for multiple times, and full extraction of spectral features is achieved.

The invention discloses an intelligent precise irrigation and nutrient regulation and control integrated system and method for rice, and particularly relates to the technical field of intelligent agriculture, and the system comprises a multi-mode sensing module, an edge computing node, an intelligent decision module, a water and fertilizer cooperative execution module and a credible monitoring module. Rice rhizosphere and leaf environment data are collected through an underground nano sensor and a canopy multispectral imaging device, data fusion and model reasoning are carried out through edge nodes, an irrigation strategy value is output in combination with a mechanism model and an AI model, and a pulse injection device is controlled to achieve precise water and fertilizer integrated operation. The system supports network disconnection operation and night model updating, and the stability and the self-adaptive capability are improved. According to the method, the environment data acquisition precision and the irrigation decision response speed are improved, the interpretability and robustness of the model are enhanced, the water resource utilization efficiency and the intelligent management level are effectively improved, and the method has wide application and popularization values.

The invention relates to the technical field of microorganisms, in particular to a disease-resistant, growth-promoting and yield-increasing strain and a microbial agent and application thereof. The strain is a strain bacillus velezensis NB21 (CCTCC (China Center for Type Culture Collection) NO: M2024979) which is found autonomously; the strain can grow at low temperature, is strong in ecological environment adaptability, can be used for preparing a single microbial agent or a compound microbial agent, and can also be used for forming a composition with other functional organic and inorganic substances. The prepared microbial agent can significantly prevent and treat diseases, promote root development of crops such as rice and corn, enhance photosynthesis, improve system resistance, increase crop yield, enhance nutrient supply and improve soil micro-ecology. The strain is unique, strong in colonization ability and diversified in function, the prepared microbial agent fermentation production technology is mature, the cost is low, the application range is wide, the yield increasing and quality improving effects are outstanding, and the microbial agent has good application prospects.

The invention discloses a water and fertilizer management method for transplanted high-yield indica type two-line hybrid rice, and belongs to the technical field of rice cultivation. Comprising the following steps: (1) applying citric acid chelated calcium magnesium phosphate fertilizer to soil before transplanting; (2) applying coated controlled-release urea and a silicon-zinc fertilizer on the third day after transplanting; (3) when the tiller number reaches 2650,000-2750,000 plants / hm < 2 >, starting intermittent light field drying for three times, and deeply applying potassium magnesium sulphate before field drying; (4) laterally and deeply applying urea and a boron-humic acid chelate when tips of two inverted leaves are exposed; (5) spraying an aqueous solution in a full heading stage, wherein the aqueous solution comprises monopotassium phosphate, chelated zinc and brassinolide. Through staged precise fertilization, special chelating / controlled release fertilizer and water and fertilizer-field drying cooperation, the method has the advantages that the fertilizer utilization rate is increased, the nutritional requirements of rice in each growth period are precisely matched, and water and fertilizer cooperative management is optimized.

The invention relates to a rice canopy optical and microwave integrated radiation modeling method, which comprises the following steps of: acquiring and uniformly processing rice canopy and environmental parameters, and constructing a same physical parameter system shared by optical and microwave models; the optical part is used for calculating bidirectional reflection factors and optical reflectivity of the rice canopy under different observation conditions based on an aquatic vegetation radiation transmission model; the microwave part is based on a vector radiation transfer equation, adopts a matrix multiplication method to calculate canopy multiple scattering, and simulates underlying surface scattering in combination with an improved integral equation model to obtain a multi-polarization backscattering coefficient; the optical reflectivity and the microwave backscattering coefficient are output under a unified parameter system, and optical and microwave collaborative forward modeling is achieved. According to the invention, a rice canopy optical and microwave integrated collaborative simulation framework is established based on a radiation transfer principle, a physical consistent basis is provided for crop parameter inversion, multi-source remote sensing data application and farmland environment monitoring, and the application prospect is wide.

The invention discloses a rice yield estimation method based on unmanned aerial vehicle remote sensing and rice spike feature extraction, which comprises the following steps: firstly, assuming a ground section RTK base station and combining an unmanned aerial vehicle remote sensing platform to determine a coverage range and a spatial position of a rice region, and then collecting a rice image and canopy height and performing deblurring processing; then using a rice ear detection model to obtain a rice ear segmentation mask, determining a pixel scaling factor to construct a rice ear feature index, and carrying out statistical analysis and identification on key features to form a feature data set; performing yield estimation by using a rice yield estimation model, and optimizing the model in combination with the actual yield; and finally, transmitting the data to a ground end to generate a thermodynamic diagram and a statistical diagram, and carrying out regional comparison and time sequence trend analysis. According to the method, the influence of the flight height on yield estimation is eliminated based on the rice ear segmentation result in combination with high-precision depth information, and the accuracy and efficiency of rice yield estimation are effectively improved.

The invention belongs to the technical field of rice panicle shielding image data processing, and provides a rice panicle shielding image restoration method and system based on a generative adversarial network, and the method comprises the steps: collecting rice panicle images at different angles and under different illumination conditions to construct a data set, employing a target detection model to carry out the positioning of a rice panicle region, and classifying the shielding types, and extracting a visible area of the rice spike by adopting a semantic segmentation network, and repairing a sheltered area under a generative adversarial network framework to obtain a complete rice spike image. The method is suitable for complex scenes such as leaf shielding, inter-panicle mutual shielding and mixed shielding, texture details and structure consistency of the repaired image can be guaranteed, acquisition of complete phenotype information of the rice panicles is achieved, and reliable data support is provided for rice yield estimation and precision agricultural management.

The invention provides a rice planting area identification method and device based on multi-temporal remote sensing data, equipment and a storage medium. The method comprises the following steps: acquiring the multi-temporal remote sensing data according to a rice phenological period; determining a spectral index sequence of the multi-temporal remote sensing data to construct a spectral index time sequence curve, and generating planting characteristic data in the target area according to a spectral change curve; determining a spectrum time sequence template matched with the rice growth rule according to the planting characteristic data, performing matching analysis on the planting mode of each land block, and marking a land block set of rice planting mode characteristics; performing cross comparison on the plot set and the rice planting reference sample to generate a rice planting mode sample set, and constructing a rice planting area identification model based on the rice planting mode sample set; and analyzing the remote sensing data of the land parcels to be identified in the research area according to the rice planting area identification model, and determining a rice planting area identification result. According to the scheme, the accuracy of rice identification can be effectively improved.

The invention discloses a fertilization recommendation method combined with a rice seedling raising fertilizer carrying technology in the technical field of agricultural production. The fertilization recommendation method comprises the following steps: S1, acquiring geology and seedling raising states; s2, presetting rice growth requirements; s3, calculating to obtain an additional nutrient amount; s4, dividing the growth cycle of the rice; s5, recommending the fertilization amount according to stages; according to the method, nutrient supply of seedling raising fertilizer is brought into a whole-growth-period fertilization plan, and accurate matching is achieved; on the premise that the yield is not affected, the fertilizing amount in the early and whole growth periods of rice is remarkably reduced, and the fertilizer utilization rate is increased; through dynamic matching of the early-stage fertilizer loading and supplying amount and the middle and later-stage fertilizer needing amount, resource waste caused by excessive fertilization is avoided, and the fertilizer cost is reduced; the nutrient proportion is scientifically distributed according to the nutrient absorption efficiency of each stage, and the fertilizer utilization rate is increased; the final yield can be improved; soil fertility indexes and historical data are combined, the profit and loss trend of soil nutrients is recognized, and cyclic utilization of the soil nutrients is achieved through dynamic matching of the sustained and controlled release fertilizer and the organic fertilizer.

The invention relates to the technical field of image classification, in particular to an edge-cloud collaborative rice disease monitoring method and system for precision agriculture, and the method comprises the steps: constructing a rice disease recognition network; a squeezing-incentive attention module and a multi-scale convolution-space attention module are introduced into a backbone network and a neck network of the rice disease recognition network; replacing standard convolution in the backbone network and the neck network with deep separable convolution; training the improved and optimized rice disease recognition network to obtain a lightweight student model; soft label distillation loss is constructed based on prediction distribution output by a pre-trained deep teacher model and a student model; a total loss function is constructed in combination with cross entropy loss, training of student models is guided through a back propagation algorithm, and a rice disease lightweight recognition model is obtained and used for recognizing rice diseases. Through edge end deployment of a lightweight model obtained through distillation, rapid reasoning of a high-precision model is realized on low-power-consumption edge equipment.

The invention provides an impeller type rice side depth variable fertilizer apparatus based on discrete elements, and belongs to the field of rice side depth fertilization. The invention aims to provide a structure capable of meeting the variable application requirement of the liquid fertilizer and provide the optimal parameter design of the structure. According to the impeller type variable fertilizer discharger, the impeller rotates to convey the fertilizer from the fertilizer inlet to the fertilizer outlet, and the opening degree of the fertilizer discharging inlet is changed by adjusting the speed of the fertilizer adjusting shifting piece, so that variable control of the fertilizing amount is realized. In order to ensure that the device can operate according to expectation, key parts are theoretically analyzed, and structural parameters are reasonably optimized and adjusted by combining simulation research on related factors of the fertilizer in the movement process; the design of the device not only provides a new implementation way for variable rate fertilization, but also contributes to improving the fertilization precision and the automation level of rice production, and promotes the development of rice agricultural mechanization to a certain extent.