35 results about "Crop management" patented technology

The invention discloses a multi-modal deep neural network model, system and method based on continuous learning. The multi-modal deep neural network model comprises a data acquisition and preprocessing module used for acquiring multi-modal data of a crop growth environment; the feature extraction module is used for extracting key agricultural features; the multi-modal information fusion module is used for effectively fusing the extracted key agricultural features; the knowledge continuous learning module is used for memorizing and storing the crop growth mode to a crop growth mode library and applying a model parameter self-adaptive updating strategy; the intelligent decision-making module is used for performing crop management decision-making based on the fusion features; and the effect evaluation and feedback module is used for evaluating the decision effect. According to the invention, the problem of knowledge forgetting of the existing AI system is solved, and the adaptability and decision accuracy of the intelligent agricultural system are improved.

The invention discloses a crop yield prediction method and device based on meteorological disaster influence, and relates to the technical field of crop management.The method comprises the steps that a yield prediction model database is constructed, and the yield prediction model database is composed of crop production areas of different disaster risk levels and corresponding crop yield prediction models; obtaining the meteorological disaster risk, the exposure of disaster-bearing bodies, the environmental vulnerability and the disaster prevention and reduction capability in the to-be-predicted crop production area, so as to determine the disaster risk level of the to-be-predicted crop production area; and searching a crop yield prediction model corresponding to the to-be-predicted crop production area in a preset database according to the disaster risk level of the to-be-predicted crop production area, and obtaining the final yield of the to-be-predicted crop production area by using the crop yield prediction model. According to the invention, the problem of low accuracy during crop yield prediction in the prior art is solved.

The invention discloses a crop disease intelligent identification method and system based on an unmanned aerial vehicle image, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data multi-dimensional collection, early disease identification, disease space-time deduction and crop management suggestion. According to the method, early disease recognition based on the spectrum sensitive vegetation index and the attention guidance double-branch network is adopted, the spectrum sensitive vegetation index capable of amplifying weak disease spot features is constructed, the weak disease features are highlighted through multispectral information enhancement and time sequence feature inhibition, meanwhile, the attention mechanism is utilized to focus on potential disease areas, and the disease recognition accuracy is improved. Therefore, the accuracy and stability of early disease recognition are remarkably improved. Disease space-time deduction combining propagation potential energy and a space-time diagram convolutional network is adopted, on the basis of considering factors such as environmental conditions, disease time sequence evolution and spatial neighborhood influence, a disease propagation path is dynamically simulated, future risks are quantified, and a visual risk prediction map and a propagation vector diagram are generated, so that agricultural management is assisted to be optimized.

The utility model discloses a novel pesticide spraying appliance for middle and later period management of high-stem crops, which comprises a first telescopic structure arranged on a pesticide spraying machine, the first telescopic structure comprises a first fixed rod and a first movable rod, a first knob for controlling the first movable rod to move is arranged at the joint of the first fixed rod and the first movable rod, and a second knob for controlling the first movable rod to move is arranged at the joint of the first fixed rod and the first movable rod. The top end of the first movable rod is connected with a hollow connecting rod, the other end of the connecting rod is fixedly connected with a three-way pipe, the two sides of the three-way pipe are connected with second telescopic structures which stretch out and draw back transversely, and each second telescopic structure comprises a second fixed rod and a second movable rod; a second knob for controlling the second movable rod to move is arranged at the joint of the second fixed rod and the second movable rod, and the end, away from the second fixed rod, of the second movable rod communicates with a spray head. The multi-dimensional pesticide spraying device realizes multi-dimensional adjustment of pesticide spraying height and width, can adapt to high-stem crops (such as corn) in different growth stages and the height of an operator, and improves pesticide spraying accuracy.

The utility model discloses an agricultural seedling irrigation device relates to agricultural seedling culture technical field, and the utility model discloses a sprinkling head, one side of sprinkling head is equipped with the deployment mechanism, is used for supplementing its nutrient solution while irrigating, and the deployment mechanism includes: the dosing assembly includes the delivery pipe of setting at one side of sprinkling head, the middle part of delivery pipe is equipped with the check valve of screwing, and the other side of check valve is equipped with the dosing tube of screwing, and the middle part of dosing tube is equipped with the spiral conveying leaf of rotating and installing, and the lower part of spiral conveying leaf is equipped with the drive assembly, and the upper part of delivery pipe is equipped with the dosing bottle of screwing, and the present application is through servo motor drive spiral conveying leaf rotation, and the nutrient solution in dosing bottle is quantitatively delivered to the delivery pipe and water flow mixing, and the one -way flow function of cooperation check valve, effectively prevent clear water backflow and pollute nutrient solution, ensure that seedling obtains accurate proportioned nutrition supplement, thereby significantly improved crop management efficiency, makes it fertilization and irrigation synchronous.

The invention discloses a crop detection method and device, equipment, a storage medium and a program product, and the method belongs to the technical field of image processing and crop management. The method comprises the following steps: acquiring a first image representing a whole area of a target farmland corresponding to field patrol time through a satellite remote sensing technology, and then acquiring a second image representing a to-be-detected crop by using an unmanned aerial vehicle technology based on the first image, the to-be-detected crop being a crop in the target farmland; and finally, based on the second image, the field patrol time and a pre-constructed farmland crop database, the field patrol efficiency can be improved while the crop growth risk can be identified in time.

The invention provides a crop irrigation and fertilization decision-making method based on probability constraint. The crop irrigation and fertilization decision-making method comprises the steps of obtaining multi-source input data, and collecting key growth index data of crops through field trials. A Markov chain is adopted to simulate distribution and correlation of different variables in meteorological historical observation data, a day-by-day meteorological sequence is generated, and the day-by-day meteorological sequence is used for reflecting the natural variation rate and uncertainty of future climate conditions. A to-be-optimized water and fertilizer management variable set is set, the crop DSSAT model is operated in each meteorological scene, and constraint conditions are constructed; and under the constraint condition, carrying out global optimization on the target function by adopting a multi-target optimization algorithm to obtain a Pareto optimal solution set. By introducing a multi-scene meteorological simulation method and combining a crop DSSAT model and a multi-objective optimization algorithm, the adaptive capacity and robustness of a crop management scheme under actual climate conditions are improved, and the scientificity and systematicness of agricultural management decisions are further improved.

A system is provided.SOLUTION: A system comprising: sensor means for measuring environmental information such as soil conditions, moisture levels, light levels, and the like; storage means for storing the environmental information collected from the sensors in a cloud database; analysis means for executing a generative AI model to perform an analysis based on the information stored in the cloud database; action plan generation means for generating an action plan for crop management based on the analysis; and prediction means for providing a prediction of future crop production.SELECTED DRAWING: Figure 1

The application discloses a vegetation light protection trigger threshold estimation method and device, the method comprising: obtaining first reflectivity data and second reflectivity data at a first time, the first time being a unit time of a preset time period in a preset area, the first reflectivity data being MODIS 11th band reflectivity data, and the second reflectivity data being MODIS 13th band reflectivity data; determining a first Rchl value according to the first reflectivity data and the second reflectivity data, the first Rchl value being the value of Rchl at the first time; obtaining solar shortwave radiation SWR data corresponding to the first Rchl value at the first time to form an Rchl-SWR corresponding relationship; determining an integrated response curve according to the Rchl-SWR corresponding relationship; and determining the light protection trigger threshold of the vegetation in the preset area according to the integrated response curve. The application can determine the light protection trigger threshold of any type of vegetation in any region in the global range, and provide scientific and technological support for crop management, climate change response and land degradation warning.

The present application relates to the technical field of field trait detection, and particularly relates to a field trait detection method based on crop breeding, comprising the following steps: determining an environment penalty value according to detected field crop color data and soil data, determining a yield reward value according to detected field crop growth data and nutrient data stored in a crop management platform, and determining a fertilizer utilization value according to detected field soil element data; performing weighted summation to generate a fertilization adjustment reward function, wherein a dynamic coefficient is determined according to crop breeding stage big data fitting of the crop management platform; updating a fertilization amount reinforcement learning model based on the fertilization adjustment reward function at regular time intervals, and determining a breeding multi-element recommended fertilization amount based on the fertilization amount reinforcement learning model by combining a Critic network with an Actor network. The present application realizes collaborative closed-loop detection of stress resistance, yield increase and fertilizer saving and field detection data mining by combining a reinforcement learning model with breeding.

A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times, and uses a trained mushroom bed model to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed. Control crop management equipment is used to perform a crop management program comprising a sequence of actions on the mushroom bed, the sequence of actions including culling actions on at least some of mushrooms determined for culling based on the mushroom bed state vectors. A trained mushroom thinning model determines mushrooms for culling based on the mushroom bed state vectors, and / or mushrooms are determined for culling when a stem-cap growth rate ratio exceeds a preconfigured threshold. An end effector for culling mushrooms has a minimum probe height-width ratio to able culling without contacting or damaging neighbouring mushrooms.

The application belongs to the technical field of data processing calculation, and provides a crop drought monitoring method based on multi-source data and application, which comprises collecting ground data; crop drought category labeling is performed on the ground data; an inversion model of meteorological data, remote sensing data and ground crop drought category is established; and the inversion model is applied to a target area for crop drought; the application provides a more comprehensive and more accurate crop drought monitoring and evaluation method by combining meteorological data, remote sensing data and ground data; and the model is based on the collection of ground samples and pays attention to the importance of ground data used in the model, because this provides a key link between satellite observation and actual field conditions, the integration of such data sources strengthens a stronger and more accurate understanding of crop drought, provides valuable insights into current crop conditions and potential future trends, and helps to improve crop management practices and reduce the impact of drought on agricultural production.

The application belongs to the technical field of unmanned agriculture, and relates to a covering plant management device for an unmanned farm and an application method thereof. The C-shaped planting assembly comprises a plurality of C-shaped planting discs, the C-shaped planting disc is provided with a C-shaped seeding groove, and a cutter is arranged in the C-shaped seeding groove. The first pulling assembly is connected with the cutter and pulls the cutter to move in a first direction along the C-shaped seeding groove. The second pulling assembly is connected with the cutter and pulls the cutter to move in a second direction along the C-shaped seeding groove. The control module is electrically connected with the first pulling member and the second pulling member. The crop management node of the covering plant accesses the control module of the unmanned farm to control the first pulling assembly and the second pulling assembly, and unmanned cutting management of the covering plant is realized. The application provides a simple device for realizing unmanned management of covering crops and can access an existing unmanned farm to realize unmanned harvesting management of covering plants.

The present invention describes an integrated agricultural data analysis system using artificial intelligence to optimize greenhouse crop management. The system includes image capture devices, an AI module for data analysis, including yield estimation and maturity prediction, cloud-based storage, an interactive user interface, and a logistics management module. The associated method has two phases: data preparation and training, followed by real-time analysis and prediction, thereby enabling precise planning and logistics management of resources and transportation, thereby promoting sustainable and economical agriculture.

The present invention provides an information processing system comprising: a sensor device for measuring a soil state, a moisture content, and an illumination amount; the server device is used for receiving and storing measurement data of the sensor device and is provided with a database; the artificial intelligence module is used for analyzing the data stored in the server device and generating predictions and suggestions related to crop management; and a notification means for notifying a user terminal of the predictions and suggestions generated by the server device.

The utility model is suitable for the field of crop management technology, provides a kind of fertilization, pesticide spraying, transportation three-in-one equipment, including travelling trolley, travelling trolley rear is equipped with load container, load container outside is equipped with feeding device, feeding device includes shovel, connecting rod and telescopic part, telescopic part, connecting rod combination forms connecting rod structure, and the angle between shovel and connecting rod is less than 80 °;Load container and travelling trolley between are equipped with conveyer belt, conveyer belt is located at load container bottom, and load container is equipped with the passage of containing conveyer belt activity;Load container inside is equipped with several fixed plate, liquid medicine conveying pipeline passes through load container and the medicine box in it are connected.

The invention relates to the technical field of crop management, in particular to a selenium-rich crop safety determination method and system and a storage medium. The method comprises the following steps: acquiring selenium element detection data of each detection part of a to-be-detected crop sample, and determining selenium form distribution corresponding to different existence forms in crops according to the selenium element detection data; calculating the relative proportion relation of organic selenium and inorganic selenium in the to-be-detected crop sample according to the selenium form distribution, and judging the safety risk level; collecting soil sample detection data corresponding to the to-be-detected crop sample, and determining the total selenium content and potential associated risk factors of the soil sample; and correcting the safety risk level judgment result according to the total selenium content of the soil sample and the potential associated risk factor, and determining the overall safety of the crop sample to be detected so as to output a corresponding safety determination report. The method is helpful for shortening the detection period and reducing the operation complexity, and is suitable for real-time monitoring and safety management of production and circulation links.

A system according to embodiments comprises a collection unit, a management unit, an analysis unit, and a provision unit. The collection unit collects data. The management unit centrally manages the data collected by the collection unit. The analysis unit analyzes the data managed by the management unit. The provision unit provides advice based on the analysis results obtained by the analysis unit.

A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times, and uses a trained mushroom bed model to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed. Control crop management equipment is used to perform a crop management program comprising a sequence of actions on the mushroom bed, the sequence of actions including culling actions on at least some of mushrooms determined for culling based on the mushroom bed state vectors. A trained mushroom thinning model determines mushrooms for culling based on the mushroom bed state vectors, and / or mushrooms are determined for culling when a stem-cap growth rate ratio exceeds a preconfigured threshold. An end effector for culling mushrooms has a minimum probe height-width ratio to able culling without contacting or damaging neighbouring mushrooms.

A system comprises a processor that is configured to receive measurement data from a sensor device configured to measure soil condition, water content, and light intensity, store the measurement data in a database, analyze the stored data to generate predictions and advice related to crop management using an artificial intelligence module, and notify a user terminal with the generated predictions and advice.

The invention relates to the technical field of crop spectral information extraction, in particular to an image spectral information extraction method and system. According to the crop identification model provided by the invention, the obtained crop hyperspectral image is identified and classified, and the model focuses on learning leaf features and fruit features of crops by introducing a crop spectral attention layer, so that the classification accuracy is improved; in addition, the invention further provides a crop health detection model which comprises a growth state detection module, a health judgment module and a pest and disease damage identification module. The growth state detection module is used for detecting the growth state of crops in the current to-be-detected crop hyperspectral image; the health judgment module is used for performing health judgment on the growth state result; and the disease and insect pest identification module is used for detecting specific disease and insect pest types of unhealthy crops so as to facilitate follow-up treatment of the crops.

The application discloses a crop irrigation control method and system based on the Internet of Things, relates to the field of agricultural irrigation technology, determines crop management range information, sets multiple sensors in the crop management range, collects crop environment information through the multiple sensors, transmits the crop environment information collected by the multiple sensors to an irrigation platform, processes and analyzes the crop environment information, controls corresponding irrigation equipment to irrigate crops according to an irrigation scheme, and gives a real-time irrigation scheme. In the process of irrigation, the application collects real-time crop environment information through multiple sensors, regenerates a new irrigation scheme at a preset time node, regenerates a new irrigation scheme every interval of time, implements irrigation according to the new irrigation scheme, can more accurately irrigate crops, guarantees the stability of crop irrigation, and does not affect the normal growth of crops in the case of water saving.

A system is provided.SOLUTION: A system comprising: means for collecting environmental information; means for transmitting the collected environmental information to a central database; a generative AI model for analyzing the information stored in the central database; means for generating crop management advice based on the analysis; and means for providing the generated advice to a farmer.SELECTED DRAWING: Figure 1

GeoSoilX is a handheld soil sensor device that integrates 10 soil quality parameters in one Internet of Things (IoT)-based unit with the support of artificial intelligence processing at the device level (Edge AI) for automatic and real-time measurements in the field. The ten parameters include Nitrogen, Phosphorus, Potassium, moisture, pH, electrical conductivity, temperature, salinity, soil color, and soil compaction level. Existing soil diagnostic systems still rely on the use of separate tools, non-uniform data acquisition processes, and do not have a centralized soil data history to monitor long-term soil dynamics, thereby reducing efficiency, data consistency, and the accuracy of soil management decisions. The GeoSoilX invention was developed to overcome these limitations through ten-in-one measurements using a single handheld device that produces more consistent data output across time and measurement locations.This device is connected to a mobile phone application and a web platform as a user interface to display measurement results and manage historical data storage centrally in a data center. Centralized storage enables continuous monitoring of soil conditions and tracking of soil condition records as a basis for data-driven decision-making in precision agriculture and plantation ecosystems, particularly to support site- and commodity-specific soil and crop management and the implementation of adaptive and sustainable nutrient intervention management.

A system is disclosed. The system includes a controller, wherein the controller includes one or more processors configured to execute program instructions stored on memory, the program instructions configured to cause the one or more processors to: receive information of a natural environment and management practices; begin automated data collection; update information of the natural environment and the management practices on a set time interval; quantify the relationship between predicted field needs and the actual field needs; calibrated based on the relationship between the predicted field needs and the actual field needs; and provide recommendations based on the predicted field needs.

Crop management system that calculates and displays the amount of each nutrient applied to a crop overtime. Fertilizer descriptions in the system's database include the amount of each nutrient in each fertilizer. Users may enter and edit a feeding schedule that describes how much of each fertilizer to apply to a crop at a sequence of feeding times. The system may combine the fertilizer descriptions and the feeding schedule data to calculate the amount of each nutrient applied at each time. Charts may be generated to display trends in nutrient fractions over time. Feeding schedules may be organized into growth stages. Users may select from a set of feeding schedule templates to create a new feeding schedule. The system may also include task management functions, and it may automatically generate and track feeding tasks based on the feeding schedules.

The invention relates to the technical field of crop growth, in particular to a simulation calculation method based on a crop physiological ecological model, which comprises the following steps: collecting crop wave frequency response data obtained by a wave frequency sensor, the wave frequency response data comprising a reflection signal, a transmission signal or a scattering signal of a crop tissue under the action of external wave frequency; and constructing a crop physiological ecological model based on the neuron structure, wherein the crop physiological ecological model comprises a plurality of physiological state units used for representing a photosynthetic process, a transpiration process, a moisture regulation process and a hormone regulation process, and a connection weight used for establishing a mapping relationship among the environmental data, the wave frequency response data and the physiological state units. According to the method, the crop wave frequency response data are collected, and the crop physiological ecological model is constructed based on the neuron structure, so that the problem that crop management is inaccurate due to the fact that the crop physiological state in traditional agriculture is invisible and not quantified is solved.

A system is disclosed. The system includes a controller, wherein the controller includes one or more processors configured to execute program instructions stored on memory, the program instructions configured to cause the one or more processors to: receive information of a natural environment and management practices; begin automated data collection; update information of the natural environment and the management practices on a set time interval; quantify the relationship between predicted field needs and the actual field needs; calibrated based on the relationship between the predicted field needs and the actual field needs; and provide recommendations based on the predicted field needs.

Disclosed herein are methods, devices, modules, and systems that can be used for automated weed identification, control, and eradication. These methods, devices, modules, and systems provide an alternative to manual cultivation or chemical herbicides. The devices disclosed herein can be configured to locate, identify weeds, and autonomously target weeds using a beam, such as a laser beam, that can burn or irradiate the weeds. The methods, devices, modules, and systems can be used for crop management or for home weed control.

The invention relates to the technical field of farmland, and discloses a farmland carbon effect footprint evaluation and calculation system and method, and the system comprises a multi-source data collection unit which is configured to obtain an agricultural machinery operation track, soil organic matter content, meteorological data and crop management records; the input end of the data fusion processing unit is connected with the multi-source data acquisition unit, and the data fusion processing unit is configured to perform space-time alignment and abnormal value elimination on the multi-source data and perform interpolation completion by adopting adjacent space-time data when data missing is detected; the input end of the hierarchical calculation unit receives the fused data and pre-stored region parameters, and executes carbon emission calculation, carbon fixing amount calculation and carbon efficiency index generation based on a standardized model; and the decision support unit is used for generating a carbon effect evaluation diagram with geographic coordinates and management suggestions according to the grading calculation result. According to the farmland carbon effect footprint evaluation and calculation system, through fusion of multi-source heterogeneous agricultural big data, dynamic perception and spatialization expression of a farmland carbon cycle process are realized, and a management scheme is generated according to the dynamic perception and spatialization expression.