7425 results about "Crop" patented technology

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention discloses an intelligent agricultural condition monitoring system and method for grain and oil crops, and relates to the technical field of agricultural condition monitoring. Farmland multi-source data are acquired and preprocessed in real time through a multi-source data acquisition module; multi-factor step-by-step fusion of farmland multi-source data is realized by using a D-S evidence theory through a multi-source data fusion module, and a multi-factor fusion evaluation index is formed; constructing a grain and oil crop environment-growth state comprehensive model based on LSTM through an environment state fusion module, and fusing the grain and oil crop environment-growth state comprehensive model with farmland multi-source data and multi-factor fusion evaluation indexes to obtain a farmland digital twin system; and a growth deviation index is analyzed and generated through the decision feedback optimization module and is fed back to the optimization system. The problems that in the prior art, data acquisition hysteresis is remarkable, multi-source heterogeneous information is isolated and difficult to fuse, and early warning fails due to lack of correlation between environment mutation and crop response are solved, and accurate monitoring and risk active intervention of grain and oil crops in the whole growth period are achieved.

Frog farming method and frog cage are provided. The method comprises: I, putting young frogs in the frog cage in spring; II, putting lure attracting mosquitoes into the frog cage into the frog cage in summer and autumn, thereafter the frogs feed on the mosquitoes; III, avoiding snakes entering the frog cage in the place where the snakes inhabit; IV, covering the frog cage with plastic film in winter, mounting an electric lamp in the frog cage, increasing the temperature of air in the frog cage to keep the frogs not hibernating and promote the frogs to grow; V, adding artificial lure if necessary. The frog cage includes a fixed frog cage and a vehicle-mounted frog cage, both including inner tiers, wherein a water basin, a terrace and a stairway communicating the upper and lower tiers are arranged on each tier, and the frog cage is covered with a mesh enclosure. The frog framing method and frog cage have the advantages that the frogs are farmed in multi-tier cages, lures are used outdoors to attract insects, pests are eliminated for crops, natural ecology is balanced, and food sources are also provided for human; cage framing of frogs is very low in cost; frogs farmed have high reproductive rate, high disease resistance and high feed conversion capacity.

The invention relates to the technical field of agricultural intelligent decision making, and discloses an agricultural intelligent analysis and decision making method based on multi-modal data, which comprises a multi-modal agent, an agent collaborative decision making technology, an intelligent decision making method and a crop identification and diagnosis method. The multi-modal intelligent agent comprises an intelligent agent framework and multi-modal capability construction; the intelligent agent collaborative decision-making technology comprises intelligent agent ecological construction, a land parcel analysis method and a land parcel economy evaluation model; the intelligent decision-making method comprises the steps of agricultural knowledge graph construction, understanding and decision making. The crop identification and diagnosis method comprises vision-language model preprocessing and intelligent identification and diagnosis. According to the agricultural intelligent analysis and decision-making method based on the multi-modal data, the technical problem in traditional agricultural decision-making is solved through processing, analysis and technical innovation of the decision-making process of agricultural multi-source data, and accurate analysis and scientific decision-making support is provided for all stages of agricultural production before production, during production and after production.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention specifically relates to the technical field of intelligent irrigation, and discloses a meteorological prediction-based irrigation dynamic regulation and control system, which comprises a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback regulation module and a man-machine interaction module, the data acquisition module integrates a water quantity acquisition terminal, a crop state acquisition terminal and a meteorological monitoring terminal, acquires multi-source irrigation decision data of a target irrigation farmland in real time, judges a water shortage risk level of the target irrigation farmland through the data analysis module, and generates an irrigation strategy through the strategy generation module; the strategy execution module executes an irrigation strategy through an electrically-driven intelligent water-saving valve, the feedback adjustment module dynamically adjusts the irrigation strategy based on sensor feedback data, and the man-machine interaction module provides a data visual interface, dynamically optimizes the irrigation strategy in combination with real-time meteorological changes and irrigation feedback, divides water shortage risk levels, and provides a water-saving management system. The utilization efficiency of water resources is effectively improved, and the applicability and reliability of the system are improved.

The invention relates to the technical field of biology, in particular to a verticillium dahliae virulence gene, a verticillium dahliae virulence protein and application. The invention discloses a verticillium dahliae virulence gene VdPHO23like. The verticillium dahliae virulence gene VdPHO23like comprises a polynucleotide sequence for coding an amino acid sequence of SEQ ID NO: 3. According to the invention, expression of the gene is inhibited in a targeted manner through an RNA interference mediated gene silencing technology so as to weaken virulence of pathogenic bacteria, and an efficient, specific and environment-friendly comprehensive prevention and control strategy is provided for verticillium wilt of crops such as cotton.

In order to solve the problems that day lily plants are densely shielded and categories are difficult to distinguish, the invention provides a field dense day lily pixel classification and picking information acquisition method. The method comprises the steps of designing a double-branch decoder fusing local and global information, constructing a progressive stepped feature mining module (PLFM) and a multi-scale hierarchical category enhancement module (PCEN), optimizing hyper-parameters through a PALA algorithm, and constructing an adaptive double-branch loss enhancement and category enhancement network (DLCE-Net) to improve semantic segmentation performance. And designing a picking point positioning algorithm (MW-GCA) of Mini-Window guided angular point analysis based on a segmentation result, screening angular point coordinates through a U-shaped window, obtaining an attitude line segment, and realizing picking point pixel coordinate and angle estimation. The method can reduce the labor loss and accelerate the engineering landing of the automatic crop picking technology.

The invention discloses a grain yield estimation method based on a land-gas coupling model and a machine learning algorithm, and relates to the technical field of agricultural meteorological prediction and intelligent decision making, and the method comprises the following steps: S1, carrying out the kernel-level algorithm fusion of a dynamic corn growth model and a weather research and forecast model, constructing a WRF-Crop coupling system, and carrying out the kernel-level algorithm fusion of the WRF-Crop coupling system; and the WRF-Crop coupling system realizes real-time interaction of transpiration and albedo change of the corn canopy and temperature, humidity and radiation data through a bidirectional data channel. According to the grain yield estimation method based on the land-gas coupling model and the machine learning algorithm, the limitation of isolated operation of a traditional dynamic corn growth model and a meteorological model is broken through by constructing a WRF-Crop bidirectional coupling model, dynamic mutual feedback simulation of vegetation growth and local climate is realized, and a crop stress response mechanism under an extreme climate event is effectively captured.

A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.

The invention discloses an intelligent farmland irrigation decision-making method based on digital twinning, and the method comprises the following steps: S1, collecting multi-source data of a farmland environment, and generating a time sequence farmland environment data set; s2, constructing a farmland digital twinborn model, and updating node and edge attribute information in real time; s3, constructing a time sequence diagram based on the updated twinborn model, and inputting the time sequence diagram to a time sequence diagram neural network; s4, optimizing structural parameters of the neural network of the time sequence diagram by applying a red-mouth blue-magpie optimization algorithm; s5, predicting the future soil water potential and the crop water demand trend by using the optimized network; s6, generating an irrigation control instruction according to the prediction result in combination with water resource supply; and S7, executing irrigation, monitoring the humidity of the land parcel and the crop state in real time, and updating the farmland digital twinborn model. According to the invention, by fusing the digital twinning, the time sequence diagram neural network and the Red-billed-blue-magpie optimization algorithm, accurate prediction and intelligent irrigation control of the future water demand trend of the farmland are realized, and the water resource utilization efficiency and the crop yield are significantly improved.

The invention belongs to the technical field of RNA biological prevention and treatment, and particularly relates to dsRNA for preventing and treating crop anthracnose and application of the dsRNA. The dsRNA is targeted inositol phosphatidylceramide (IPC) synthetase coding gene dsRNA, and is obtained by transcription of a part of sequence (SEQ ID No.1) of an inositol phosphatidylceramide (IPC) synthetase coding gene CaAUR1, the gene segment is a segment of a target gene for preventing and treating crop anthracnose, and the dsRNA obtained by transcription can obviously reduce pathogenicity of mango anthracnose bacteria, and has the effects of keeping mango fresh, preventing and treating anthracnose. And the compound can be used for preventing and treating the crop anthracnose or preparing products for preventing and treating the crop anthracnose.

The invention relates to the technical field of intelligent control, in particular to a monopotassium phosphate intelligent control method and system.The method comprises the following steps that rhizosphere phosphorus and potassium concentration changes are obtained, whether the rhizosphere phosphorus and potassium concentration changes are in a critical rate interval or not is judged, an effective absorption time period identifier is generated, and an organ amplification ratio is analyzed to judge a cooperation period; and extracting a stalk concentration ratio, comparing the stalk concentration ratio with a reference interval to generate an application inhibition identifier, fusing information to judge a fertilization window, adjusting the concentration according to the root hair amplification and the water consumption rate, and generating a control instruction set. According to the method, the effective time period of phosphorus and potassium nutrient absorption of the crops is determined through crop rhizosphere phosphorus and potassium ion concentration difference calculation and average treatment, the objective interval of crop nutrient absorption rate indexes is defined, blind fertilization is avoided, the pertinence and accuracy of nutrient absorption and utilization of the crops are enhanced, the excessive application state of nutrient elements is dynamically identified, and the crop quality is improved. The risk of excessive nutrients caused by nutrient enrichment of crops is prevented, and resource waste and environmental burden are effectively reduced.

The invention discloses a tobacco growth environment multi-parameter intelligent regulation and control method driven by the Internet of Things, and relates to the technical field of agricultural Internet of Things and environment intelligent regulation and control, and the method comprises the steps: based on the environment parameters of a tobacco planting area collected in real time and crop stem flow data, combining the current growth stage and quality target of tobacco, and obtaining a tobacco growth environment multi-parameter intelligent regulation and control result; dynamically generating a target concentration change track of the key metabolite representing the internal physiological activity of the tobacco; taking the target concentration change track as an optimization target, carrying out reverse solution by using a crop physiological mechanism model for describing the relationship between the environment and the physiological process, and calculating a target track of environment parameter set values which are required for realizing the optimization target and change along with time; according to the target trajectory, combining with a pre-established equipment disturbance propagation model, and solving a field domain control inversion problem to generate a coordination action sequence of the environment regulation and control equipment; and the cooperative action sequence of the equipment is executed to control the corresponding environment regulation and control equipment to work.

The invention discloses a polymeric phosphorus slow-release compound fertilizer and a preparation method thereof, and relates to the technical field of agricultural fertilizers, the polymeric phosphorus slow-release compound fertilizer comprises an inner core and a coating layer, wherein the inner core is prepared from the following components in percentage by weight: 30 to 50 percent of modified ammonium polyphosphate, 15 to 25 percent of urea-formaldehyde slow-release nitrogen, 10 to 20 percent of potassium humate-coated potassium chloride, 2 to 5 percent of amino acid chelated trace elements and 1 to 3 percent of seaweed extract; through the synergistic effect of the citric acid modified ammonium polyphosphate and the three layers of functional envelopes, the phosphorus utilization rate is increased, and the problem that phosphorus is easily fixed by soil is effectively solved. The fertilizer can automatically adapt to different soil environments through the pH responsive coating design, and 30-40% of phosphorus can be quickly released in acid soil to meet the early needs of crops; phosphorus is continuously supplied through activation of the phosphate solubilizing bacteria under a neutral condition; and the fertilizer efficiency is prolonged to 120-150 days by virtue of an outer layer slow release mechanism in an alkaline environment.

The invention relates to the technical field of intelligent agriculture, in particular to a variable pesticide spraying method and system based on crop canopy recognition. The method comprises the following steps: acquiring canopy structure data; sparse anchor point data are obtained according to the canopy structure data; performing canopy segmentation according to the sparse anchor point data to obtain canopy segmentation data; performing canopy feature extraction on the canopy segmentation data to obtain canopy feature data; performing penetration demand measurement estimation according to the canopy feature data to obtain penetration demand measurement data; obtaining nozzle parameter data; obtaining nozzle control data according to the nozzle parameter data and the penetration demand measurement data; and performing operation monitoring according to the nozzle control data to obtain nozzle operation data so as to perform spraying uniformity feedback to obtain spraying uniformity data. According to the invention, through fusion of canopy structure identification and spraying path modeling, precise matching of the pesticide spraying direction, the spraying amount and the penetration demand is realized, and the spraying uniformity and the pesticide utilization efficiency are significantly improved.

The invention discloses an agent-driven self-adaptive farming management system, which belongs to the technical field of intelligent agriculture and artificial intelligence crossing, and comprises a farming management system, and the farming management system constructs an agricultural management agent with high autonomy, self-adaptive learning ability and scientific decision basis. The agricultural management agent comprises a sensing module, a planning and reasoning module, an action module and an incremental learning module; the system has flexibility, scientific preciseness and high autonomy and adaptability, can realize the precise, autonomous and efficient management of the whole cycle of agricultural production in a thousand-place and thousand-strategy mode, remarkably improves the resource utilization efficiency and crop output, and greatly reduces the manpower operation and maintenance cost.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention provides a special charcoal-based slow-release fertilizer for sandy soil as well as a preparation method and application thereof, and belongs to the technical field of fertilizers. The special charcoal-based slow-release fertilizer for the sandy soil is prepared from amino modified biomass charcoal, urea, urea formaldehyde, monoammonium phosphate, potassium sulfate, polyglutamic acid, fulvic acid, clay, attapulgite, a compound microbial agent and sodium alginate. The compound microbial agent is prepared from halomonas (ACCC 11681), bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094), and the compound microbial agent is prepared from bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094). The invention further provides a preparation method and application of the special charcoal-based slow-release fertilizer for the sandy soil. The special charcoal-based slow-release fertilizer for the sandy soil can remarkably improve the water and fertilizer retention capacity of the sandy soil, improve the farmland productivity of the sandy soil, save fertilizer and water input and improve the yield and quality of crops.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention relates to the field of microorganisms, in particular to a mortierella alpina strain MA with a plant growth promoting function and application of the mortierella alpina strain MA. The strain has remarkable nitrogen fixation, siderophore production, auxin (IAA) production and cellulase activity, can inhibit various soil-borne pathogenic bacteria such as phytophthora capsici and fusarium, and remarkably improves the disease resistance and growth performance of crops such as pepper. The strain has various functions and broad-spectrum action, is suitable for replacing part of chemical fertilizers and pesticides in intensive agriculture, and realizes green and efficient planting of crops.

The invention provides an irrigation water demand prediction method based on meteorological soil crop multi-source feature fusion, and relates to the field of data prediction, and the method specifically comprises the steps: firstly collecting farmland region multi-source feature data, and constructing an original data set; then, a nonlinear disturbance relation between farmland characteristics is extracted through a high-order disturbance encoder module, and a structure energy tensor is introduced to describe high-order structure relevance; thirdly, a dynamic gating characteristic generator module is introduced, a disturbance gain factor and a time coupling factor are combined to generate a gating signal, and dynamic screening of multi-source characteristics is achieved; then constructing a cross-variable interaction modeling module, and generating a feature embedding vector to model a complex coupling relationship among weather, soil and crops; and finally, outputting an irrigation water demand predicted value by adopting a multi-layer sensing network in combination with channel mapping and a cross memory mechanism, so that efficient dynamic prediction of the farmland water demand is realized, and the water resource utilization efficiency and the accurate regulation and control capability are improved.

The invention relates to the technical field of agricultural fertilizers, discloses a seaweed extract-functional microorganism compound slow-release fertilizer and a preparation method thereof, and solves the problems in the background art. The seaweed extract is a compound containing enteromorpha polysaccharide and alginic acid extract; the functional microorganism is a complex microbial inoculant composed of phosphate solubilizing bacteria, nitrogen-fixing bacteria, phosphate solubilizing bacteria and bacillus; the slow-release carrier is prepared by coating the seaweed extract and the functional microorganisms with sodium alginate-chitosan microcapsules; the signal molecule is selected from at least one of polyamine substances and jasmonic acid methyl ester; the fertilizer has the advantages that the utilization rate of the fertilizer can be increased, growth and reproduction of soil microorganisms are promoted, the stress resistance of crops is improved, and the requirements of different soil types and planting environments can be met.

The invention relates to the technical field of agricultural remote sensing measurement, and discloses an unmanned aerial vehicle hyperspectral online calibration measurement method for agricultural remote sensing, which comprises the following steps: acquiring hyperspectral data of a target crop and reference vegetation in the same scene in real time through an unmanned aerial vehicle, and extracting a characteristic wave band reflectivity difference value delta R, according to the comparison result of the time sequence change rate of the delta R and the preset threshold value, crop physiological changes and environmental interference are dynamically distinguished, and online calibration is achieved. A self-consistent calibration mechanism is constructed by means of the time sequence stability of crop spectral characteristics, the illumination mutation influence can be eliminated without depending on an external reference substance, and the accuracy of the calibration result is improved. And meanwhile, a lodging scene is effectively identified through synchronous analysis of a gravity vector and spectrum drift, so that the system can still keep accurate tracking of a real physiological state of a crop under an extreme working condition.

The invention discloses a remote monitoring and management rice seedling raising irrigation plant protection system, computer equipment and a medium, and relates to the technical field of rice seedling raising irrigation, the remote monitoring and management rice seedling raising irrigation plant protection system comprises a sensing acquisition module, an edge processing module, a data fusion module, a prediction modeling module, an execution control module, a user interaction module and an energy safety component; the sensing acquisition module is used for acquiring soil humidity, temperature, pH value, air temperature and humidity, illumination intensity, wind speed and direction, rainfall and crop image information through multiple types of sensors deployed in a rice seedling raising field, and outputting original environment data; the edge processing module is connected with the sensing acquisition module and is used for preprocessing the original environment data, generating preprocessed data and uploading the preprocessed data to a cloud server through an NB-IoT protocol; and the data fusion module is deployed in the cloud server and is used for receiving the preprocessed data from the edge processing module.

The invention discloses a photovoltaic auxiliary greenhouse cultivation system which comprises a greenhouse body composed of a framework system and a photovoltaic ceiling, and the photovoltaic ceiling is laid on the top of the framework system, provided with a light splitting type solar thin film cell panel set with adjustable light transmittance and laid in a partitioned mode according to the illumination requirement. The dynamic light compensation system, the temperature balance system and the energy storage system are arranged at the top of the framework system, the energy storage system is used for storing energy and supplying power, the dynamic light compensation system is used for supplementing light for each indoor crop area according to photosynthetically active radiation and sun illumination conditions, and the temperature balance system is used for ventilating and humidifying the greenhouse; and the control machine is arranged on the greenhouse main body and is electrically connected with the dynamic light compensation system, the temperature balance system and the energy storage system so as to receive the monitoring data and control the operation of each electric appliance element. According to the system, lithium battery energy storage and a grid-connected inverter are integrated, the energy self-sufficiency rate is optimized, and the space utilization rate is increased through spectrum customization and three-dimensional planting; the method is suitable for tilled land shortage areas, and gives consideration to clean energy production and agricultural efficient output.

The invention provides a multi-dimensional data fusion soil fertilizer application system and method, and relates to the technical field of soil fertilizer application, and the system comprises a data collection module which is used for collecting multi-source original data including soil, weather, crops and fertilizer in real time; the data preprocessing module is used for preprocessing the multi-source original data; the data fusion module is used for generating a comprehensive index reflecting the soil fertility state and the crop demand; the fertilization decision module is used for generating an optimal fertilization scheme; the execution control module is used for controlling the variable fertilizer applicator and the drip irrigation equipment to execute precise fertilization operation; and the effect monitoring and feedback module is used for evaluating the decision effect and optimizing the nutrient leaching model. According to the invention, through multi-objective optimization based on the nutrient leaching model and the genetic algorithm, the fertilizer cost, the environmental risk and the agricultural machinery operation constraint are considered while the fertilizer requirement rule of crops is met, and the optimal fertilization scheme considering both the economical efficiency and the ecological property is output.