1199 results about "Paddy field" patented technology

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

The invention relates to the technical field of target detection, in particular to a rice disease and insect pest target detection algorithm based on Mamba and YOLOv8, which comprises the following steps: acquiring a rice field RGB image and preprocessing to generate an abnormal image; inputting the abnormal image into a MedVSSM module, extracting global long-distance dependency features through a Mama state space model, extracting local features such as a polypide contour and scab texture in combination with CNN, and fusing and outputting multi-scale features; performing cross-layer semantic enhancement and path aggregation through a C2F-MedVSSM module, and generating an optimized feature map through dynamic weight adjustment; and the YOLOv8 module outputs a target detection frame, a category and confidence, and a final detection result is obtained after non-maximum suppression processing. According to the method, the Mama global modeling capability and the CNN local feature extraction advantages are fused, feature complementation is realized through a dynamic weight mechanism, the detection precision under the scenes of complex illumination, leaf shielding and the like is effectively improved, and the method has a practical application value for rice disease and pest prevention and control.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention discloses a gleying rice field intelligent irrigation and drainage regulation and control method based on reinforcement learning. The method comprises the steps that meteorological data, soil data and field surface water level data are collected; calculating crop state information based on the meteorological data and the soil data by using a crop growth model; inputting the meteorological data, the soil data, the field surface water level data and the crop state information into an offline training strategy model, outputting action suggestions, and performing verification and decision making according to preset regulation and control logic in combination with the meteorological data, the soil data, the field surface water level data, an external data source and agronomic rule base data; a control strategy is generated, irrigation, drainage or oxygenation is executed, and irrigation and drainage regulation and control are completed. Compared with the prior art, the method has the advantages that priority regulation and control logic for ORP / pH value real-time feedback is established, a prospective pre-drainage mechanism for rainfall forecast is fused, water layer constraint in the growth period and conditional filtering rules in the fertilization safety period are fused, and a perception-decision-execution closed-loop control system is formed.

The invention relates to the technical field of soil function improvement, in particular to a rice field soil recarburization and capacity expansion method based on a charcoal-based organic fertilizer. The method comprises the following steps: crushing a biomass raw material, pyrolyzing and cooling to obtain biochar; immersing the biochar in a functional bacteria solution containing actinomycetes and bacillus for activating treatment to obtain modified biochar; mixing the modified biochar and decomposed straw, composting and fermenting to obtain organic fertilizers with different particle sizes; the large-particle-size organic fertilizer is applied to deep soil, the soil is covered with soil after straw is laid on the soil, and the small-particle-size organic fertilizer is applied to surface soil. Through layered application, the large-particle-size biochar repairs the deep soil structure and expands the carbon storage capacity, and meanwhile, the carbon stability is improved through carbon-nitrogen synergistic metabolism driven by functional bacteria, so that the carbon immobilization efficiency is improved, meanwhile, soil recarburization and expansion are achieved, the contradiction between carbon sequestration and structure degradation and nutrient imbalance in a traditional recarburization and expansion technology is solved, and the soil recarburization and expansion efficiency is improved. Synergic improvement of a carbon sink function and agricultural productivity is promoted.

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 rice field irrigation and drainage decision-making method and system based on human-in-loop reinforcement learning, and the method is realized based on reinforcement learning, and the method comprises the steps: collecting real-time environment parameters of a target rice field in a current decision-making period; constructing an environment state vector based on the real-time environment parameters; constructing an irrigation drainage decision model of a continuous action space based on a soft actor commentator algorithm, wherein the model outputs an action according to the environment state vector; when it is judged that the output action deviates from a reasonable strategy or has a potential risk, a human-in-loop collaborative intervention mechanism in which a human expert participates is introduced, the output action is corrected, and a human-in-loop reward signal is generated through expert intervention; an output action is executed, environment feedback and a multi-target weighted reward signal containing a human-in-loop reward signal are collected, loop iteration is performed based on a closed-loop control system, and the irrigation and drainage decision model is updated; and obtaining irrigation and drainage decision output of the current decision period according to the updated irrigation and drainage decision model.

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 provides a rice transplanting robot suitable for a small rice field and a control method thereof, and belongs to the technical field of agricultural machinery. Comprising a moving part and a rice transplanting assembly, the moving part comprises a machine body, a plurality of mechanical legs and driving assemblies corresponding to the mechanical legs, the mechanical legs are connected with the machine body through the corresponding driving assemblies, the rice transplanting assembly comprises a rice seedling storage box, a mechanical arm, a mechanical claw, an angle sensor and a finger pressure sensor, and the rice seedling storage box is arranged on the machine body; one end of the mechanical arm is rotationally connected with the machine body, the angle sensor is arranged on the mechanical arm, the mechanical claw is arranged at the other end of the mechanical arm, the finger pressure sensor is arranged on the mechanical claw, and the mechanical arm has multidirectional rotation freedom degrees. And the insertion depth of the seedlings is adjusted according to readings of the angle sensor and the finger pressure sensor. The technical problems that existing rice transplanting equipment is limited in use in small rice fields due to structural defects, and the survival rate of planted seedlings is low can be solved.

The invention discloses a system for accurately removing abnormal plants in rice breeding based on intelligent image recognition, and relates to the field of agricultural intelligent equipment.The system comprises an image acquisition module, an abnormal plant detection module and an impurity removal control module, the preprocessing module is used for preprocessing rice field plant images and transmitting the preprocessed rice field plant images into the abnormal plant detection module, the abnormal plant detection module is used for analyzing and processing the rice field plant images and identifying to-be-processed abnormal plants, and the impurity removal control module is used for receiving the positions of the to-be-processed abnormal plants and controlling the impurity removal structure to remove impurities.

The invention provides a rice irrigation online learning forecasting method and system, the method is realized according to a pre-constructed physical mechanism-neural network hybrid model based on physical mechanism model prediction and neural network error correction fusion, and the method comprises the following steps: S1, obtaining real-time environment data of a current decision period of a target rice field; s2, on the basis of the real-time environment data of the current decision period, forecasting a paddy field water layer depth predicted value of the next decision period through a physical mechanism-neural network hybrid model, and generating an irrigation drainage forecast in combination with a crop irrigation drainage mode; s3, real-time environment data of the target rice field in the next decision period after irrigation drainage forecast is executed are obtained, training samples are constructed and put into an experience playback pool, and the physical mechanism-neural network hybrid model executes online learning based on the experience playback pool; and S4, when the next decision cycle starts, returning to S2 until a preset stop condition is met.

The invention discloses a rice field crayfish body surface cleanliness improving method based on accurate aquatic plant management and protection and ecological induction, and relates to the technical field of crossing of aquaculture and ecological agriculture. The lifting method comprises the steps that submerged plants are planted in the rice field in February every year, and the submerged plants are trimmed regularly in the cultivation process so that the top ends of the submerged plants can be kept under the water surface; shrimp seeds are put into the rice field, and feed is fed regularly; in the subsequent breeding process, during regular feeding of feed, feeding of crushed pomacea canaliculata is carried out; and after the young crayfishes grow up and shell, catching the crayfishes to obtain the crayfishes with high body surface cleanliness. According to the method, the growth state of the aquatic plants is precisely controlled to construct a natural clean field domain, the functional bait is fed to increase nutrition, meanwhile, friction cleaning is carried out through the feeding behavior, precise fishing is carried out based on the molting period, and therefore the body surface cleanliness of the crayfishes bred in the rice field is improved.

The invention discloses a rice field insect pest detection method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the following steps: receiving the soil humidity and environment temperature of a rice field, and determining a temperature and humidity abnormal region of the rice field based on the soil humidity and environment temperature of the rice field; acquiring field image data in the temperature and humidity abnormal area of the rice field; processing the field image data in the temperature and humidity abnormal region of the rice field based on morphological closed operation and morphological gradient operation to obtain processed field image data, inputting the processed field image data into a pre-established YOLOv5s + target detection algorithm model, and outputting to obtain a pest and disease damage identification result; and performing image splicing generation based on the field image data in the temperature and humidity abnormal region of the rice field and the pest and disease identification result to obtain a disaster grading map.

The invention relates to the technical field of remediation for synergistic treatment of rice field soil arsenic and greenhouse gas emission reduction, in particular to a zero-valent manganese nitrate synergistic soil remediation agent and application thereof to remediation of rice field arsenic pollution and inhibition of greenhouse effect. The invention provides a technical scheme for synergistically repairing arsenic pollution in the rice field and inhibiting the greenhouse effect based on zero-valent manganese / nitrate, an electron transfer shunt network driven by manganese circulation is constructed, a slow release reaction-dynamic regeneration-electron shunt three-in-one system is constructed, and synergistic interaction of arsenic fixation and greenhouse gas emission reduction is realized. According to the technical scheme, the potential regulation and control contradiction between arsenic fixation aerobic and carbon reduction anaerobic is successfully broken, and an innovative solution is provided for multi-target remediation of rice field soil. According to the self-sustaining circulating system, the specific surface area of the manganese oxide is stabilized at 50m < 2 > / g to 120m < 2 > / g, and the regeneration rate is gt; 95%. Experiments show that compared with a single technology, the synergistic treatment group improves the arsenic fixing efficiency to 100%, and the comprehensive emission reduction intensity of greenhouse gas reaches 76%.

The invention relates to the technical field of data processing, in particular to a rice field gate anti-clogging intelligent monitoring control method, which comprises the following steps of: acquiring multi-dimensional monitoring data of a gate at each monitoring moment in real time, and forming a multi-dimensional monitoring data sequence by the multi-dimensional monitoring data of the gate at each monitoring moment in each operation process; one gate operation process corresponds to one multi-dimensional monitoring data sequence; for any gate operation process, obtaining an optimized gate clogging coefficient of any gate operation process according to the multi-dimensional monitoring data sequence corresponding to any gate operation process; the method comprises the steps of obtaining an optimized gate clogging coefficient of each gate operation process, obtaining a gate clogging trend index by using the optimized gate clogging coefficient of each gate operation process, and carrying out gate clogging prevention intelligent monitoring control according to the optimized gate clogging coefficient and the gate clogging trend index of the current gate operation process. And early-stage, quantitative and trend monitoring of the clogging state of the gate is realized.

The invention discloses a rice growth period distinguishing method and an intelligent irrigation control device. Belongs to the technical field of crossing of intelligent agriculture and irrigation and water conservancy engineering and particularly relates to the technical field of rice growth period judgment and intelligent irrigation. The technical problem that an accurate and effective rice growth period judgment method for guiding intelligent rice irrigation is lacked at present is solved. The method specifically comprises the following steps: calculating a rice growth process index through an improved accumulated temperature model; and constructing a multi-factor coupling growth period judgment method, and judging the current rice growth period through the rice growth process index. The system comprises a sensing unit, a judgment and decision-making unit, an electric execution unit and an input and power supply unit, the judgment and decision-making unit receives the environment data of the rice field and the water layer depth data of the rice field, judges the current growth period of the rice according to a rice growth period judgment method, calculates the current water layer deviation and generates a control instruction to the electric execution unit.

The invention discloses a method and system for evaluating the influence of long-term fertilization on rice field soil heavy metals, and the method comprises the steps: obtaining rice field soil data through a sensor, and obtaining a standardized soil physicochemical property data set and a heavy metal content data set; according to the standardized soil physicochemical property data set and the heavy metal content data set, obtaining a feature vector set reflecting correlation between soil physicochemical properties and heavy metal behaviors; aiming at the feature vector set, obtaining a prediction model for representing the influence of the fertilization mode on the heavy metal mobility; if the heavy metal effective state content output by the prediction model exceeds a preset bioavailability threshold value, classifying the soil physicochemical property data set, judging the influence weight of the soil pH value and the organic matter content on the cadmium mobility, and obtaining the classified soil environment risk grade; and according to the classified soil environment risk levels, obtaining comprehensive rice field soil heavy metal pollution evaluation indexes. The pertinence and effectiveness of pollution control are remarkably improved.

The invention discloses a special organic-inorganic compound fertilizer for improving the yield and quality of rice in a rice field, and belongs to the technical field of agricultural fertilizers. The compound fertilizer comprises a three-layer structure consisting of a porous core layer, a quick release coating layer and a biodegradable film, wherein the porous core layer is prepared from ammonium polyphosphate, potassium humate, nano charcoal modified chicken manure, mineral source potassium fulvate and sodium carboxymethyl cellulose-bentonite composite colloid; the quick-release coating layer is prepared from sulfur-resin double-layer coated urea, amino acid chelated zinc, alginic acid complex boron, silica sol, ammonium molybdate, 5-aminolevulinic acid and nano cerium oxide; the biodegradable film is prepared from a film forming solution and a bioactive microbial agent. Through the design of a three-layer structure and the combination of an organic-inorganic-microorganism synergistic technology, accurate matching of nutrient release and a rice fertilizer requirement rule is achieved, meanwhile, soil health and environmental protection are considered, and an innovative solution is provided for green high-yield rice planting.

The invention belongs to the technical field of microbial resource development and straw biological decomposition. The invention discloses cold-resistant pseudoarthrobacter psychrotolans CKON-4, and the preservation number of the cold-resistant pseudoarthrobacter psychrotolans CKON-4 is CGMCC (China General Microbiological Culture Collection Center) No.29378. The cold-resistant pseudoarthrobacter psychrotolans CKON-4 has the advantages that the preservation number The strain CKON-4 is preserved in the China General Microbiological Culture Collection Center (CGMCC) on December 25, 2023, the China General Microbiological Culture Collection Center is called CGMCC for short, the address of the China General Microbiological Culture Collection Center is No.3, Yard 1, Beichen West Road, Chaoyang District, Beijing, and the preservation number of the strain is CGMCC No.29378. The strain disclosed by the invention has very high lignocellulose degradation activity, and the decomposition rate of straws can be remarkably improved; the strain is separated from rice field soil, can grow in the pH value range of 5-9 and the temperature range of 4-37 DEG C, can tolerate the salt concentration of 10%, and is a bacterium with wide pH application range, salt resistance and cold resistance. The strain can play an important role in lignocellulose degrading enzyme production, straw biological decomposition and compost fermentation.

The invention discloses a rice mature period judgment method based on image recognition, and relates to the technical field of computer vision and agricultural informationization, and the method comprises the following steps: carrying out the continuous shooting of a rice field at a set initial image capture frequency through a high-definition visible light camera carried by an unmanned plane, and obtaining the original image data of rice ears; according to the method, a dynamic regulation and control mechanism is constructed based on an image recognition and machine learning fusion technology, and intelligent sensing of the rapid swinging state of the rice ears and self-adaptive adjustment of image acquisition parameters are achieved. The space and time dimension features are extracted through feature engineering, the model evaluation result is combined, the image capture frequency and the shutter speed are dynamically optimized, gale interference is effectively dealt with, the image definition and the recognition accuracy are improved, high-quality data support is provided for rice mature period judgment, and the system environment adaptability is enhanced.

The invention provides the application of the OsLNHG1 gene in regulating and controlling the nitrogen utilization efficiency and the yield of the rice, in a low-nitrogen paddy field, the plant height of an OsLNHG1 gene overexpression plant is remarkably increased, and the tiller number is a core agronomic character for directly determining the yield of the rice. In a normal nitrogen paddy field, the plant height and the effective tiller number of an OsLNHG1 gene overexpressed plant are remarkably increased, a core gene resource is provided for cultivating a new variety of fertilizer-saving high-yield rice, and the gene has great significance for agricultural sustainable development.

The invention belongs to the technical field of crop pollution prevention, and particularly relates to a rice heavy metal adsorption and isolation method based on iron agent regulation and control. According to the method, the iron agent is applied to the polluted rice field soil to induce the soil-rhizosphere interface to form an iron film, heavy metals (such as As, Cu, Pb and Cd) are fixed by virtue of adsorption and complexing effects of the iron agent, and migration of the heavy metals to rice roots is reduced, so that accumulation of the heavy metals at edible parts (leaves and ears) of the rice is reduced; the tolerance of the rice in a polluted environment is enhanced; and the risk of heavy metals in a food chain is reduced. Experiments show; through treatment of an iron film formed by a certain iron-based concentration, accumulation of the heavy metals in the rice leaves can be remarkably reduced, and the accumulation amount of the heavy metals in the rice leaves is far lower than the accumulation amount (1 / 40-1 / 120) of the heavy metals in rice roots. The invention provides an effective solution for farmland soil remediation and crop safety production.

The invention belongs to the technical field of modern agricultural precise planting and plant nutrition regulation and control. The invention provides a method for precisely applying selenium to oilseed rape leaf surfaces based on dynamic regulation and control in a growth period, enriching selenium, reducing cadmium and increasing yield. A sodium selenite solution is sprayed to the leaf surfaces in the key growth period of oilseed rape. The method can effectively improve the selenium content of each part of the oilseed rape, reduce the cadmium content of grains and guarantee the yield, is suitable for safe production of oilseed rape replanting in medium-light cadmium-polluted rice fields in the south of China, can be widely applied to standardized production of selenium-rich oilseed rape in main winter oilseed rape producing areas in the Yangtze River basin, and has wide application prospects. The method has important practical significance in improving agricultural quality and efficiency, guaranteeing food safety and promoting income increase of farmers.

The invention relates to the field of disease control, and discloses a semantic segmentation and grading method for cross-scale identification of rice black dwarf disease and lodging, and the method comprises the following steps: S1, collecting rice field data under different elevation scales, and processing the data through software to obtain image data; s2, based on the altitude information of the DSM, determining a classification threshold by combining elevation error correction and height distribution difference statistical analysis, and constructing a DSM-guided auxiliary supervision mask; s3, constructing a cross-scale semantic fusion network, and inputting the image data set in S1 and the auxiliary supervision mask in S2 into the network for training to obtain a rice black dwarf disease and lodging detection model; and S4, predicting by using the rice black dwarf disease and lodging detection model in the S3. By constructing a digital surface model and a digital orthoimage with aligned pixels and combining elevation error correction, accurate association of rice field data under multiple elevation scales can be realized, and support is provided for rice black dwarf disease and lodging identification.

The invention discloses a soil risk assessment method for a red-soil rice field, particularly relates to the field of soil risk assessment, and aims to solve the problems that an existing method cannot accurately identify the hypoxia risk below a plough layer and the influence of the hypoxia risk on a root system. The method comprises the following steps: constructing a joint monitoring mechanism of heat flux and oxygen concentration, extracting metabolic heat release power and an oxygen consumption trend, identifying a reduction dominant region below a plough layer, fitting a space boundary of the reduction dominant region, and establishing a three-dimensional distribution diagram of an oxygen deficit dominant region; the method comprises the following steps: extracting an overlapped space boundary of a root zone and an anoxic zone in combination with a crop root system distribution condition, analyzing a contour expansion rate and a root zone change characteristic in a continuous monitoring time period, and constructing a soil degradation trend identification parameter set; and finally, performing score mapping on the parameter sequence according to a preset degradation judgment weight, generating a soil degradation risk grade map covering the whole red soil rice field region, and having a soil risk expression capability with high spatial resolution and high judgment precision.

The invention discloses an electric quantitative fertilization device for rice and a monitoring system, and relates to the technical field of agricultural machinery, the electric quantitative fertilization device comprises a support frame, a storage box and a fertilization execution mechanism, the support frame is used for supporting the storage box and the fertilization execution mechanism; the storage box is used for storing fertilizer; the fertilization executing mechanism comprises a quantitative conveying assembly and a distribution assembly, the quantitative conveying assembly is arranged below the fertilizer outlet of the material storage box and used for quantitatively conveying fertilizer, the distribution assembly is connected with the output end of the quantitative conveying assembly, and the distribution assembly is used for distributing the fertilizer into different distribution channels and discharging the fertilizer from the material outlet; the monitoring system is used for calculating the fertilization amount required by the current paddy field according to the collected paddy field data and the state data of the device, generating a fertilization control instruction to realize quantitative fertilization, and supporting a user to remotely check and adjust fertilization parameters. The use safety is higher, and the fertilization efficiency and quality are improved.

The invention relates to the technical field of image processing, and relates to an unmanned aerial vehicle-based rice field soil heavy metal dynamic monitoring method and system, and the method comprises the steps: obtaining image data of a rice field region, and determining a first heavy metal index of rice field soil in the image data through a preset water body absorption index; dividing the rice field area into a plurality of rice field sub-areas, and obtaining a second heavy metal index corresponding to each rice field sub-area; determining a spatial stability coefficient and a time trend coefficient by using the second heavy metal index; determining a comprehensive quantitative index by using the spatial stability coefficient and the time trend coefficient, and determining a heavy metal monitoring result of the soil in the rice field region according to the comprehensive quantitative index; according to the method, the influence of environmental interference on data accuracy is reduced by inhibiting water body signals, after soil heavy metal indexes of all areas are obtained, time-space associated comprehensive indexes are constructed to quantify the change condition of heavy metal pollution in the areas, and dynamic monitoring of the rice field heavy metal is implemented according to the quantitative result.

The invention discloses a rice disease and insect pest image real-time identification and decision-making system based on deep learning, and particularly relates to the technical field of agriculture. A multi-modal fusion identification module; a resistance decision engine module; a dynamic prevention and control execution module; and a feedback optimization module. According to the invention, through a new intelligent plant protection normal form of intelligent perception-collaborative decision-precise execution-closed loop evolution, a multi-spectral imaging and microscopic feature synthesis technology is utilized, the bottleneck of traditional diagnosis is broken through, early-stage precise recognition of diseases and insect pests is realized, a medicament-pathogenic bacteria-natural enemy tripartite game model and a multi-objective optimization mechanism are innovatively constructed, and the method has a wide application prospect. The ecological regulation and control capability of the rice field is activated while the risk of drug resistance is avoided, it is ensured that prevention and control measures accurately reach a target by means of the three-dimensional profiling spraying and natural enemy synergistic release technology, residual feedback and model self-iteration are fused in the whole process, a prevention and control closed loop which is more intelligent is formed, and an evolvable digital solution is provided for rice green production.

The invention discloses a preparation method of a controlled-release fertilizer for mechanical transplanting rice seedling raising subsoil and application of the controlled-release fertilizer to replace field fertilization, and belongs to the technical field of rice cultivation. A coating of the controlled-release fertilizer is prepared from the following components in parts by weight: 20 to 60 parts of low-density polyethylene resin, 10 to 20 parts of ethylene-vinyl acetate copolymer, 5 to 30 parts of synthetic resin biodegradation inducer, 3 to 15 parts of starch, 10 to 70 parts of talcum powder and 0.2 to 2 parts of nonionic surfactant. The controlled-release fertilizer comprises urea and a compound fertilizer which are respectively coated by the coating film. According to the controlled-release fertilizer for the machine-transplanted rice seedling raising subsoil, a coating material is optimized, the release inhibition period of the controlled-release fertilizer is 20-30 days, normal growth of rice seedlings is ensured, and the rice growth requirements are accurately met. When the method is used for rice planting, a seedling raising subsoil fertilizer carrying method is adopted, integration of rice transplanting and fertilization is achieved, and on the basis of ensuring high and stable yield of rice, the fertilizer application cost and the risk of nitrogen and phosphorus pollution emission in the rice field are reduced.