510 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 an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of agricultural environment intelligent regulation and control, in particular to an agricultural environment intelligent regulation and control method and system based on a cloud platform, and the method comprises the steps: collecting farmland environment parameters and crop image data through a multi-source sensor, filtering the farmland environment parameters and crop image data through an edge computing node, and transmitting the filtered farmland environment parameters and crop image data to the cloud platform; preprocessing the farmland environment parameters and the crop image data, and extracting time domain and space domain features; an agricultural environment state index prediction model is constructed based on a multi-head self-attention encoder, training of the agricultural environment state index prediction model is completed through historical data, the agricultural environment state index prediction model is deployed as a cloud service API, farmland environment parameters and crop image data input in real time are processed online, a control instruction is generated according to a prediction result, and the control instruction is sent to the cloud service API. Accurate and intelligent regulation and control of the agricultural environment are completed through Internet of Things protocol issuing equipment. Through dynamic feature modeling and multi-index collaborative optimization, the precision of agricultural environment regulation and control and the resource utilization efficiency are remarkably improved.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

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 provides an intelligent breeding factory big data decision optimization system and method. The system comprises a database, a model library, a data acquisition module, a biomass prediction module, an intelligent decision module and an execution module. The specific optimization method comprises the following steps: firstly, obtaining a corresponding predicted biomass I and a predicted biomass II based on collected breeding parameters and biological images, and fusing to obtain a final predicted biomass; corresponding target breeding parameters are obtained according to the final predicted biomass, the target breeding parameters are compared with breeding parameters collected in real time, and when the difference value of one or more breeding parameters of the same type exceeds a preset threshold value, corresponding decision optimization information is generated based on the breeding parameter in the target breeding parameters; and finally, adjusting the breeding environment and feeding work according to the decision optimization information. According to the invention, the matched aquaculture data is obtained by predicting the biomass of the current fish in real time, and the corresponding decision optimization information is generated, so that the intelligent level of the current aquaculture is improved.

The invention relates to the field of intelligent agriculture and intelligent construction, and discloses an agricultural monitoring system and method based on an intelligent helmet, and the system comprises a wearable terminal, an active sampling control module, an image recognition processing module, a multi-modal fusion module, a recognition reasoning module, and a result output module. The method comprises the steps of collecting a field crop image and environment data; calculating an uncertainty index of an identification result according to the current image; if the uncertainty is high, adjusting the acquisition angle of the camera and re-sampling; constructing the image and the agricultural environment data into a multi-modal graph structure and carrying out graph neural network processing; performing dynamic trend identification on a fusion result, and outputting a crop state; and outputting an identification result through a helmet display or linking an agricultural control system to execute related operations. According to the invention, the capabilities of identification display, voice communication, operation guidance and system integration in a complex environment are improved, and the problems of poor real-time performance and weak interaction of an existing scheme are solved.

The invention relates to the technical field of agricultural environment control, in particular to an automatic environment regulation and control system for planting traditional Chinese medicinal materials, which comprises an environment quality evaluation module, an environment regulation and control demand analysis module, a planting mode optimization module and an intelligent regulation and control module. According to the method, environmental changes can be comprehensively understood by comprehensively evaluating the environmental quality, monitoring and analyzing the volatility of environmental data and judging the actual influence on the root growth of the traditional Chinese medicinal materials, a more stable and suitable growth condition is provided for the traditional Chinese medicinal materials, and fine adjustment of the environmental data is realized by simulating multi-environment combination, so that the accuracy of the traditional Chinese medicinal materials is improved. The method not only optimizes the rhizosphere environment, but also effectively promotes the absorption of the traditional Chinese medicinal materials to the target nutrient elements, enhances the growth efficiency and quality of the crops, dynamically regulates and controls the environmental parameters, further improves the nutrient absorption capacity of the traditional Chinese medicinal materials, ensures the health of the root system and the overall development of the crops, and improves the economic benefits of the crops. And synchronous improvement of production efficiency and crop quality is ensured.

The invention discloses a robot path planning method based on deep reinforcement learning, and relates to the fields of intelligent agriculture, path planning, robots and the like. The method comprises the following steps: firstly, sensing a farm environment, defining a state space and an action space of a robot, and setting a multi-modal weighted combined reward mechanism and an experience playback buffer area; a learnable weight coefficient is introduced into a Critic network loss function in a traditional DDPG algorithm, an entropy regularization item is added into a target function of an Actor network, then a differential game is selected through an adaptive attenuation # imgabs0 # greedy strategy to generate a control strategy or a DDPG algorithm to generate an action, finally the action or the control strategy is executed, network parameters and target network parameters are updated, and a target network is obtained. And dynamically updating the experience playback buffer area. Compared with other path planning methods, the method improves the adaptability of path planning to a dynamic environment, and also has good efficiency and safety in a complex agricultural environment.

The invention discloses an intelligent fertilization and irrigation regulation and control device and method based on deep learning. The method comprises the following steps: S1, constructing an agricultural environment original data set; s2, preprocessing the original data set to generate an agricultural data set with consistent time and space; s3, constructing a multi-layer neuromorphic calculation model which comprises a photosynthetic layer, a root system layer and a transpiration layer; s4, performing correlation analysis based on the photosynthesis dynamic curve, the root system water and fertilizer absorption curve and the transpiration-moisture supply curve, and constructing a photosynthesis-root system-transpiration synergistic curve; s5, on the basis of the photosynthesis-root system-transpiration synergistic curve, calculating a promoting factor of photosynthesis on root system absorption and a regulating factor of root system absorption on transpiration intensity, and constructing a self-adaptive regulation and control function; and S6, carrying out time sequence analysis on the self-adaptive regulation and control function, and formulating a final fertilization and irrigation regulation and control scheme. According to the method, a multi-layer neuromorphic calculation method is adopted, the photosynthesis-root system-transpiration cooperative relation is accurately modeled, and intelligent fertilization and irrigation regulation and control are achieved.

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.

Onboard and / or offboard crop stabilization for reducing movement within an agricultural environment to enhance robotic manipulation for crop harvesting.

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 relates to the technical field of agricultural management, in particular to a smart farm environment regulation and control method and system based on big data analysis, and the method comprises the following steps: obtaining an instantaneous temperature value of the upper surface of a leaf and an instantaneous temperature value of the lower surface of the leaf through an infrared temperature sensor, obtaining an instantaneous value of stomatal conductance through a stomatal conductance sensor; and acquiring a water vapor pressure difference value of the air cavity through a water vapor pressure sensor, aligning a time sequence by adopting a multi-source data fusion algorithm, eliminating noise, and generating a dynamic physiological data set. According to the method, the leaf temperature difference, the air hole conductance and the air cavity water vapor pressure difference are collected, a real-time dynamic data set is constructed, a temperature difference amplification signal is extracted, transpiration enhancement triggering, wind speed increasing, water mist particle size decreasing and wind speed and particle size linkage regulation and control are analyzed in combination with the trend, a regulation and control weight is constructed by introducing multiple regression analysis, and the wind speed and the particle size are dynamically regulated according to the weight. A leaf temperature difference regression base line is guaranteed, the regulation and control accuracy and physiological adaptability are improved, and the environment self-adaptive regulation capability is enhanced.

A farming method may be shown and described. In an exemplary embodiment, plants may begin in a germination phase. Next, plants are brought to a nursery for a period of time before optionally being transplanted to one or more subsequent nurseries. Finally, plants are transplanted to a greenhouse where they may grow until they are ready for harvest. In an exemplary embodiment, the nursery phases may be vertical farms while the greenhouse phase may be a traditional, hydroponic, or other type of farm which may receive sunlight. AI may be implemented to optimize environmental conditions and robotics may be used to harvest the plants. Plants may be indexed to efficiently expedite plant growth and optimize the time and plant density / spacing in each phase.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

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 relates to the field of agricultural environment monitoring, and provides an agricultural non-point source pollution early warning method and system and electronic equipment, and the method comprises the steps: obtaining a first pollution monitoring parameter of a to-be-monitored region; predicting a second pollution monitoring parameter of the to-be-monitored area in a set time period; when the second pollution monitoring parameter meets the first early warning condition, first-level early warning is carried out, and when the second pollution monitoring parameter does not meet the first early warning condition but meets the second early warning condition, second-level early warning is carried out; and calculating the pollution contribution rate of the to-be-monitored area to the downstream area through the graph neural network, and if the pollution contribution rate meets a third early warning condition, performing third-level early warning. The invention discloses an agricultural non-point source pollution monitoring method and device, aims to overcome the defects that pollution monitoring means in the prior art are difficult to accurately identify and early warn pollution and cannot position pollution sources, can perform different levels of early warning on agricultural non-point source pollution through a graded early warning mechanism, and can realize real-time monitoring, dynamic early warning and accurate treatment of regional pollution.

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 discloses an intelligent agricultural plant protection operation control system based on a big data Internet of Things, and the system comprises a sensing layer which is an agricultural environment parameter collection module which collects the environment parameters of a farmland in combination with a fine-grained expert splitting technology; the crop growth state monitoring module is used for monitoring the growth state of crops in real time; a network layer: an Internet of Things communication module which is responsible for data transmission between the sensing layer and the platform layer; the platform layer comprises a big data processing and analysis module which is used for cleaning, integrating, analyzing and mining data acquired by the sensing layer in combination with a hybrid expert model in a MoE architecture; the intelligent decision support module is used for constructing a dynamic decision optimization model and dynamically adjusting a decision scheme; the plant protection operation management module is used for designing a hybrid solver of a space-time heuristic rule and integer programming, generating a preliminary operation plan, and then adjusting and optimizing the preliminary operation plan in real time according to real-time data in combination with a dynamic decision optimization model and an expert model in the MoE architecture.

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 discloses an intelligent agricultural environment monitoring method and a light supplement adjusting system, and particularly relates to the technical field of agricultural monitoring. An edge computing device is utilized to extract photosynthesis efficiency irregular features and illumination intensity change rate fluctuation features in a data processing module, and a delay degree evaluation module evaluates the delay abnormal degree between real-time environment data and a light supplement adjustment decision. The light supplementing control module is used for deeply analyzing delay data in a fixed time period and dynamically adjusting the illumination intensity and spectral distribution of a light supplementing lamp, so that the problem of insufficient response caused by high delay and single parameter analysis in a traditional method is effectively solved; the accuracy and the real-time performance of agricultural environment monitoring are remarkably improved, the crop growth condition is optimized, and therefore the agricultural production efficiency and the crop quality are improved.

The invention discloses an animal epidemic prevention processing method and system based on a block chain, and the method comprises the steps: building a farm basic database, formulating an epidemic prevention time table in combination with animal variety characteristics and geographic position climate conditions, and determining epidemic prevention time nodes of a farm; the method comprises the following steps: acquiring farm environment data, animal health data and epidemic prevention operation records in real time through Internet of Things equipment, and generating an epidemic prevention execution data stream; comparing the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judging the implementation condition of epidemic prevention measures by setting a threshold value and a rule; according to the epidemic prevention execution data flow and the implementation condition of the epidemic prevention measures, a random forest algorithm is adopted to analyze the epidemic prevention effect, and an epidemic prevention effect evaluation report is generated; and an epidemic prevention effect evaluation report and epidemic situation early warning information are encrypted, stored and shared through a block chain, and data support is provided for epidemic prevention decision making. According to the invention, intelligence, precision and traceability of epidemic prevention management of the farm are realized.

The invention relates to the technical field of intelligent agricultural data auditing, in particular to a non-corresponding auditing system for data sharing of the Internet of Things, which comprises a data acquisition association unit, a multi-modal data fusion unit, a self-adaptive calibration unit and a calibration evaluation feedback unit. The multi-modal data fusion unit uses a convolutional neural network to extract features and obtains accurate fusion feature vectors through a multi-modal feature fusion model and multiple optimization technologies and deep fusion data, and the self-adaptive calibration unit dynamically adjusts calibration model parameters by means of a reinforcement learning algorithm and a deep Q network in combination with historical data so as to obtain a multi-modal feature fusion model. And the calibration evaluation feedback unit evaluates the calibration effect, feeds back optimization if the expectation is not reached, and provides decision support based on the calibrated data, thereby effectively solving the problem of data accuracy caused by the complex agricultural environment, and improving the reliability of data auditing.

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.