350 results about "Farming environment" patented technology

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The invention provides an agricultural automation linkage control method and system based on artificial intelligence. Belongs to the technical field of agricultural automation control. The method comprises the following steps: acquiring agricultural environment data and agricultural automation equipment data, and preprocessing the acquired agricultural environment data; performing feature extraction on the preprocessed agricultural environment data, and constructing an agricultural environment feature vector; and performing agricultural environment state analysis according to the agricultural environment feature vector to obtain an agricultural environment state evaluation result. Through preliminary control simulation, reinforcement learning algorithm optimization control strategy and linkage control simulation, and continuous iteration optimization of equipment control strategy, the control strategy better meets the actual production requirement, the accuracy and effectiveness of agricultural automation equipment control are improved, manual intervention is reduced, and the production cost is reduced.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention relates to the technical field of agricultural environment intelligent monitoring, and discloses a cultivated land crop adaptability evaluation and planting suggestion system, which comprises a plurality of distributed microenvironment decision-making units, each unit acquires soil humidity and illumination data in real time through an environment sensor, and slope information acquired by a tilt angle sensor is combined to determine the adaptability of cultivated land crops. The local processor calculates entropy values representing crop stress risks based on a dynamic weight adjustment algorithm, and entropy value interaction and collaborative decision-making among units are realized through a low-power-consumption communication network. Therefore, the system can autonomously identify the local hydrological stress risk, forms a cooperative response network based on the physical law between units, and achieves the precise perception and intelligent decision-making of a complex farmland environment.

An internet-of-things communications network deployed within an environment is disclosed. The system may be used to analyze emissions related data within an agricultural environment and provide a calculation regarding a monitored environment's carbon emissions. The system has a communications network allowing for the transmission and reception of information. The network may include a plurality of devices capable of obtaining information related to agricultural actions, an access point, and a remote database. The communications networks may deploy a modified Wi-Fi HaLow communications protocol. Further, a data analysis module may be implemented to analyze and report information related to the monitored environment. The data analysis module may be utilized in partitions to optimize processing of the system where some execution of the data analysis module may occur at the access point and other execution occurs at the remote database.

The invention discloses a facility vegetable tomato leaf miner pest comprehensive prevention and control system, and relates to the technical field of facility agriculture pest prevention and control, and the system comprises a space distribution obtaining module which obtains the space distribution data of a pest invasion path in a facility agriculture environment, and determines the protection level demand intensity of different regions; the protection parameter calculation module is used for calculating specification parameters and installation density of insect prevention grids in all areas according to the protection level requirement strength, and if the pest density of the peripheral area exceeds a preset threshold value, the aperture of the insect prevention grids is adjusted to the minimum specification, and a first-layer electric control isolation net protection barrier configuration scheme is obtained; the comprehensive control system for the greenhouse vegetable tomato leaf miner pests can effectively deal with the complexity and variability of pest invasion, and the pest control effect and sustainability of the greenhouse agricultural environment are remarkably improved.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses a ventilation method and device based on precise monitoring of the environment in a farm and a storage medium, relates to the technical field of analysis control, and constructs a three-dimensional pollution concentration field model by collecting data such as the pollution gas concentration, suspended particles, temperature and humidity, airflow velocity and the like of a manure ditch in real time and combining the spatial layout of a breeding house. On the basis of the model and ventilation constraints, a virtual ventilation strategy composed of multiple sets of air supply speeds, air directions and air opening degrees is automatically generated, simulation is conducted, disturbance scores are calculated, and the optimal strategy is selected to be applied to ventilation control. Environmental parameters are continuously monitored in the ventilation process, if it is detected that pollutants rise or temperature and humidity are abnormal, the strategy generation and screening process is automatically restarted, and dynamic closed-loop optimization adjustment of a ventilation scheme is achieved; dynamic ventilation control can be carried out according to the actual manure environment, the influence of gas backflow and pollution disturbance is reduced, and the influence on the farm environment is reduced; the influence on the growth health of livestock and poultry is reduced.

The invention relates to the technical field of agricultural environmental protection and groundwater pollution remediation, in particular to an agricultural non-point source groundwater pollution remediation technology screening system and method, and the system comprises a barrier technology screening module, a degradation technology screening module, a removal technology screening module and a cost accounting module; the method comprises the following steps: quantitatively evaluating the pollutant blocking capability of a blocking technology to farmland infiltration through a soil hydraulics equation, screening an aeration zone efficient degradation technology by utilizing a Fick law and a Monod kinetic model, and verifying the economic feasibility of an aquifer removal technology based on a shadow engineering method and a full life cycle cost model; the method comprises the following steps: determining optimal technologies of farmland infiltration, an aeration zone and an aquifer in sequence through each module, and generating a feasible scheme in combination with cost accounting. The problems that a traditional technology is poor in adaptability and high in cost in an agricultural scene are solved, and full-chain precise screening from a pollution source to a tail end and economic and feasible repairing technology combination are achieved.

The invention provides an intelligent cattle farm feeding management system, which belongs to the technical field of intelligent agriculture and animal husbandry, and comprises a data acquisition module, a data processing module, a control module, a user interface module, a communication module and a storage module. The system obtains cattle farm environment and cattle physiological behavior data in real time through a multi-source sensor and image acquisition equipment, intelligent analysis and decision generation are carried out through algorithms such as machine learning, reinforcement learning and fuzzy logic, and automatic management of functions such as feed feeding, environment regulation and control and health early warning is achieved. The system has the characteristics of comprehensive perception, intelligent decision making and accurate execution, the cattle farm feeding efficiency can be effectively improved, cattle health is guaranteed, and the operation cost is reduced.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.

The invention provides a security optimization encryption method, system and device for data in a facility agriculture scene and a medium, and belongs to the technical field of agricultural informatization security, and the method comprises the steps: carrying out the dynamic division of security levels of data collected in a facility agriculture environment according to the data source device type, data content sensitivity and real-time requirements; distributing an encryption algorithm for the collected data according to a mapping relation between the security level and the encryption algorithm; secret keys are generated and managed based on an identity encryption mechanism, and then secret key fragmentation storage is carried out in combination with a trusted execution environment; when encryption is needed, whether the current trusted execution environment is safe or not is judged, and when the current trusted execution environment is safe, secret keys of the fragments are recombined, the multi-source heterogeneous data are encrypted through the distributed encryption algorithm, and encrypted data are generated; and carrying out secure transmission on an adaptive communication protocol after packaging the encrypted data. According to the method, the data security level is dynamically divided, the encryption algorithm is flexibly selected, the resource consumption is reduced, and the real-time performance and security are improved.

The invention discloses a soil heavy metal form analysis method and system based on multi-modal data fusion and small sample learning, and belongs to the field of agricultural environmental science. The soil heavy metal form analysis model based on multi-modal data fusion and small sample learning is operated for direct output, multi-modal data isomerism and a low-data-volume scene can be considered, the method is suitable for multi-scene and multi-range soil heavy metal form proportion prediction, and a scientific basis is provided for accurate pollution control to output the to-be-detected heavy metal form proportion.

Methods for optimizing path planning for agricultural vehicles are provided. In one embodiment, a field is defined, passes completely covering the field are created, the ends of each pass are transformed into nodes, distance and time matrices for the nodes are computed, and an optimization problem incorporating the time and distance matrices, agricultural constraints, and time window requirements is formulated. Prior to solving the optimization problem, verification that a feasible solution to the optimization problem exists is performed, and constraints are adjusted if a feasible solution does not exist. The optimization problem may be solved using a quantum annealing process to achieve an optimized route that performs well in an agricultural setting.

The invention relates to a heterogeneous cluster cooperative task allocation and path planning method for an agricultural unmanned aerial vehicle, and the method comprises the following steps: S1, carrying out the system modeling and initialization, and carrying out the modeling of a cooperative data collection task of an unmanned aerial vehicle mechanism cluster in an agricultural environment as a decentralized partially observable Markov decision process; s2, defining a multi-dimensional space, wherein the decentralized part observes a global state space, a joint action space and a local observation space of a Markov decision process; s3, designing a mixed reward function; s4, performing cluster strategy training; and S5, performing online distributed execution. The method effectively solves the problems that an existing method is poor in environment dynamic change adaptability, does not fully consider the heterogeneous characteristics of the unmanned aerial vehicles and does not fully consider decision-making and execution separation, and remarkably improves the operation efficiency, safety and collaborative intelligence level of an agricultural unmanned aerial vehicle cluster in a complex unstructured environment.

The invention relates to the technical field of tillage equipment, and discloses a direct-drive power system of a low-altitude rotary cultivator and a control method of the direct-drive power system. A data acquisition module of the system acquires environment and operation data in the operation process of the low-altitude rotary cultivator; the expected cultivation parameter generation module builds a cultivation adaptive adjustment model, and inputs the collected data into the model to obtain expected cultivation parameters; the initial-stage control signal module generates an initial-stage control signal of the direct-drive power system based on the expected cultivation parameters; the initial execution module executes an initial tillage action according to the initial control signal; the deviation collection module collects actual cultivation data after the initial cultivation action and calculates the deviation between the actual cultivation data and the expected cultivation parameters; the control correction module corrects the initial control signal based on the deviation and generates a final control signal. The system adapts to a complex farming environment through self-adaptive adjustment and dynamic correction, the farming quality and the equipment stability are improved, and the precise farming requirement is met.

The invention provides an ecological pig raising system based on intelligent monitoring and precise feeding, and belongs to the technical field of intelligent monitoring and precise feeding. According to the scheme, multiple types of sensors are deployed in a breeding environment, environment parameters are continuously collected, the collected environment parameters are preprocessed and preliminarily analyzed, and the environment parameters are analyzed to be detected; the processed environment parameters are sent to the central processing unit through the network transmission device, the central processing unit analyzes and evaluates the health state of the pigs, an individualized feeding plan is made according to the health state of the pigs, the behaviors of the pigs are monitored in real time, and abnormal conditions are responded. The problems that the quality of the breeding environment cannot be comprehensively known, and the safety and stability of data transmission cannot be guaranteed are solved.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention discloses a pig weight estimation method based on vision, and belongs to the field of intelligent breeding, and the method comprises the following steps: obtaining an initial image set of a breeding pig based on a multi-view camera system; performing image preprocessing on the initial image set to obtain mask coordinates; registering the multi-view point cloud of the boar depth image into a complete three-dimensional model by using a skeleton constraint ICP algorithm based on the mask coordinates, and determining the body structure representation of the boar based on the complete three-dimensional model; and inputting the body structure representation of the boar, the back fat texture features of the boar and the kinetic energy index into a double-branch prediction model to obtain a final weight estimation value of the boar. According to the invention, the influence of complex environment and light change of a farm is overcome, and accurate non-contact measurement of the boar weight is realized.

The invention is suitable for the technical field of agricultural automation, and provides a wheel-foot combined type picking robot and a control method thereof.The robot comprises a wheel-foot dual-mode moving chassis, a sensing system and a working module; the wheel-foot dual-mode mobile chassis adopts a four-foot bionic layout, each foot end is wrapped by a foot end shell, wheels are arranged at the bottoms of the foot end shells, and hub driving motors are arranged in the foot end shells; the sensing system comprises a laser radar, a depth camera and a control unit, the laser radar is installed at the front end of the wheel-foot dual-mode mobile chassis, and the depth camera is installed at the execution end of the working module; and the working module is mounted on the wheel-foot dual-mode mobile chassis. Through an innovative wheel-foot composite structure and a layered cooperative intelligent control strategy, the picking robot achieves full-process automation from efficient movement to stable operation in an unstructured agricultural environment, and the picking robot has extremely high application value and popularization prospects.

The invention discloses a greenhouse climate multi-target intelligent control method fusing rule constraint and reinforcement learning, and relates to the technical field of agricultural environment control, and the method comprises the following steps: building a temperature and humidity dynamic model based on an energy conservation law, and coupling an external environment disturbance term to obtain a temperature and humidity dynamic model; establishing a mapping relation among the temperature, the relative humidity and actuator input data, generating multiple groups of interval training data based on the mapping relation, constructing an intelligent decision model, and screening out an optimal actuator state vector by taking the deviation degree of an environment target value and an interval range as an evaluation criterion; and setting a difference mediation threshold value of mutual exclusion operation for safety correction, adopting environment adjustment difference and energy consumption minimization as optimization objectives, designing a multi-objective reward function, and determining an optimal control instruction, thereby updating an intelligent decision-making model, realizing accurate and efficient greenhouse climate control, enabling the comprehensive control of temperature and humidity to be more accurate and reasonable, and improving the reliability of the greenhouse climate control. And the energy consumption is reduced.

The invention discloses a dexterous hand fruit and vegetable grabbing control method based on fuzzy variable impedance control. The method comprises the following steps: firstly, establishing a multi-finger collaborative kinematics model based on a spinor theory, and determining fingertip poses, grabbing force of each finger and multi-finger resultant force distribution; on the basis of force sealing, an optimization model with the minimum contact force as a target is constructed, an NSGA-II algorithm is adopted for solving, and the minimum grabbing force needed by stable and lossless grabbing is obtained; establishing an impedance control model in a Cartesian space, enabling a grabbing system to be equivalent to a mass-spring-damping system, analyzing the influence of an impedance parameter on grabbing performance, and determining an inertial parameter and a damping coefficient as key adjustment objects; fuzzy control is further introduced, the grabbing force error and the change rate of the grabbing force error serve as input and output inertia and damping adjustment amount, a fuzzy self-adaptive variable impedance controller is constructed, and dynamic optimization of impedance parameters and accurate tracking of the grabbing force are achieved. The fruit and vegetable grabbing device can be effectively applied to rapid, stable and lossless grabbing of various fruits and vegetables in an unstructured agricultural environment.

The invention belongs to the technical field of intelligent agriculture, and provides an agricultural environment monitoring method and system based on the 5G Internet of Things, and the method comprises the steps: extracting growth feature data of crops in an accept region in a plurality of historical periods from historical crop growth records, building a growth stage recognition model based on a machine learning model, and carrying out the recognition of the growth stage; identifying growth stages of all crops in the acclimatization area, and judging whether all crops in the acclimatization area are in the same growth stage or not; compared with the standard parameter range of the crop growth stage, the method effectively avoids the resource waste of the traditional uniform environment regulation and control, remarkably improves the targeting and efficiency of the environment regulation and control, provides datamation decision support for the growth of the crops in the optimal environment, and assists the fine management and yield improvement of agriculture.

The invention discloses a soybean producing area soil combined pollution micro-ecological risk assessment method and system, and belongs to the technical field of agricultural environment monitoring. The method comprises the following steps: acquiring soil pollutant data and microbial community data, calculating a risk entropy, constructing a micro-ecological health index, and respectively obtaining a first result and a second result by utilizing a risk assessment neural network and a comprehensive model combined with principal component analysis, a clustering algorithm and a hunter optimization algorithm; and determining a final risk level and high-risk pollutants after key factor matching and weighted correction mechanism verification. According to the method, the problems of single evaluation dimension, insufficient dynamic adaptability and the like in the prior art are solved, accurate diagnosis and risk early warning of soil combined pollution are realized through multi-source data fusion and multi-method cooperation, the evaluation accuracy and decision-making efficiency are improved, and technical support is provided for soybean producing area soil micro-ecological risk management and control and agricultural sustainable development.

The invention relates to the technical field of agricultural environment information, and discloses a rice field nitrogen and phosphorus loss amount remote regulation and control method oriented to environment data. The method comprises the following steps: acquiring historical nitrogen and phosphorus loss data, real-time meteorological data and water and fertilizer operation information of the rice field; constructing a mechanism constraint sub-model fusing nitrogen migration and transformation kinetics and phosphorus adsorption and desorption balance; performing multi-source feature extraction on the meteorological data to generate a standardized environment vector; inputting the lightweight residual convolutional neural network to output an initial prediction value; on-line fine adjustment is carried out through a Lagrangian multiplier method in combination with the mechanism model, and a corrected predicted value conforming to substance conservation is generated; and when the predicted value exceeds a threshold value, generating a drainage gate opening degree and fertilization plan adjustment instruction, and issuing the instruction to an edge terminal for execution through the narrowband Internet of Things.