102 results about "Crop management" patented technology

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention provides a planting agriculture carbon sink metering method and system and computing equipment. The method comprises the following steps: establishing a dynamic farmland ecosystem carbon sink model according to biomass monitoring data, soil carbon content data, meteorological data and crop management measure records of a farmland ecosystem; based on the dynamic farmland ecosystem carbon sink model, analyzing carbon fixation efficiency under different crop types and different farming modes in a farmland ecosystem; and calculating the total carbon sink amount of the farmland ecosystem. According to the technical scheme provided by the invention, the dynamic farmland ecosystem carbon sink model is established, so that the carbon absorption capacity of the farmland ecosystem can be more accurately quantified, and the carbon sink metering precision is improved.

The invention relates to the technical field of soil physical property detection, in particular to a soil moisture content intelligent monitoring control method and system, and the method comprises the steps: multi-modal sensor data collection: deploying a multi-modal sensor array in a to-be-detected soil area; interference compensation and correction; self-adaptive calibration is carried out; water phase state distinguishing and soil moisture content index calculation; 4, soil moisture content evaluation: comprehensively evaluating the actual water content, the effective water content, the soil moisture content index and the water phase state information obtained in the step 4, and setting different grade threshold values to obtain a soil moisture content evaluation result; and intelligent control decision and execution: taking the soil moisture content evaluation result as input, and generating a corresponding control decision. The intelligent monitoring and controlling system for the soil moisture content can monitor the soil moisture condition in real time and can improve the precision and efficiency of agricultural irrigation through intelligent compensation, calibration and decision optimization, and therefore the purposes of water saving and efficient crop management are achieved.

The invention provides a germination form measurement method and system based on improved U-Net to realize automatic measurement, reduce labor cost and significantly improve efficiency, thereby optimizing tomato planting and management. According to the application, the seed germination form acquisition system is used for acquiring images of seed culture and seed germination processes, a data set is constructed, and the data set is used for training the improved U-Net model. According to the invention, the up-sampling of the model is replaced by the DBB module, and the CAA is introduced into the jump joint and the decoder. The MIOU index and the MPA index of the model can be respectively improved by 2.03% and 0.39% compared with those of the basic U-Net. According to the method, based on the segmentation result of the improved model, the radicle length can be extracted in combination with the canny algorithm. Compared with the ImageJ measurement result, the correlation coefficient of the model can reach 0.991, and the measurement efficiency is improved by 300 times. According to the method, the influence of drought, salinization and different molds on tomato radicle germination can be evaluated, and an efficient solution is provided for nondestructive monitoring and crop management in precision agriculture.

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.

Disclosed aspects relate to systems and methods for assisting farmers with crop management. For example, systems may be configured to estimate crop damage, identify plant disease, estimate plant disease severity, and / or identify weeds. In some aspects, automated systems may process images of an area having crops, for example using one or more crop model. The models may identify one or more property of the crop, which can be used to better manage crops.

The invention discloses a crop leaf disease diagnosis method and system driven by a large language model. Comprising the following steps: firstly, extracting a leaf disease image of a target crop and corresponding text semantic information from a basic crop management file so as to obtain an initial crop disease knowledge database; then, the initial crop disease knowledge database is optimized and updated in combination with visual understanding of a large language model; and then, constructing a crop disease classification agent model, inputting a to-be-diagnosed leaf disease image into the crop disease classification agent model, performing crop disease diagnosis by the crop disease classification agent model according to the crop disease knowledge database, and outputting a disease identification result. Under the condition that large-scale image training is not carried out, disease diagnosis can be accurately carried out on the crop disease images, disease image diagnosis requirements of different crops can be met, and the problem that existing work is insufficient in potential in the aspects of zero sample application and expandability is solved.

The present application relates to the technical field related to greenhouse planting, and particularly relates to a crop management and control method and apparatus for a smart greenhouse, and a device and a medium. The method comprises: acquiring environmental data and crop data; inputting the environmental data and the crop data into a preset regulation and control model to obtain an optimal planting scheme and / or an optimal environment control strategy; and on the basis of the environment control strategy, regulating and controlling preset smart remote control devices in a smart greenhouse. By means of the configuration in the solution provided in the present application, accurate control of a planting environment can be performed by collecting environmental data and crop data in a timely manner, thereby increasing the crop growth rate and improving the production efficiency. Furthermore, a preset regulation and control model is used in the present application, so as to automatically optimize a planting scheme and an environment control strategy on the basis of different environmental conditions and crop requirements, thereby reducing manual intervention and management costs, and thus improving the production efficiency and economic benefits.

The invention belongs to the technical field of agronomy experiment devices, and particularly relates to a mulch-mulched soil environment simulation device which comprises a closed space, an air inlet and an air outlet. The mulching film sample is arranged in the closed space; the top of the soil simulation assembly is in contact with the bottom of the mulching film sample, and the soil simulation assembly is used for simulating a soil environment covered by the bottom of the mulching film sample; the natural environment simulation assembly is communicated with the closed space, the natural environment simulation assembly is used for simulating environmental conditions of the top of the mulching film sample, and the environmental conditions comprise temperature and humidity parameters, illumination parameters, rainfall parameters and wind speed parameters. According to the invention, researchers can accurately and controllably carry out experiments indoors, simulate soil states under different mulching film materials and environmental conditions, and deeply understand the influence of mulching film mulching on key parameters such as soil moisture, temperature and gas exchange. Scientific basis is provided for agricultural production and crop management, and sustainable development of agriculture is promoted.

The invention discloses a distributed improvement method and device for farmland crop irrigation water productivity. The method comprises the steps that soil physical indexes and nutrient indexes are obtained; performing management area division on the farmland by using a fuzzy clustering method based on the soil physical indexes and the nutrient indexes; sorting the contribution rate of each soil index to the crop irrigation water productivity in each management partition by using a partial least square discriminant analysis method to obtain a driving factor of the crop irrigation water productivity; using a boundary line analysis method to determine a regulation threshold value of a crop irrigation water productivity driving factor in each management area; calculating actual stage water consumption of crops in each management area, establishing an irrigation system optimization model, and performing multi-objective optimization solution on the irrigation system optimization model by applying a genetic algorithm to obtain an optimized irrigation system; and combining the regulation and control threshold value of the crop irrigation water productivity driving factor with the optimized irrigation system to obtain a farmland distributed crop management scheme, so that the crop irrigation water productivity is improved.

Disclosed herein are methods, devices, modules, and systems that can be used to accurately track and subsequently target objects of interest relative to a moving body, such as a vehicle. The object tracking methods can be implemented by a detection system in which a sensor is coupled to the moving body. The targeting system can be used to target objects tracked by the detection system, such as for automating crop cultivation or maintenance. The devices disclosed herein can be configured to locate, identify, and autonomously target weeds with a beam, such as a laser beam, that can burn or irradiate the weeds. These methods, devices, modules, and systems can be used for agricultural crop management or home weed control.

The present invention discloses a method for selective crop management in real time. The method comprises steps of: (a) producing a biosensor plant, said biosensor plant comprises a visual biomarker, said biomarker is encoded by at least one modified genetic locus comprising (i) preselected reporter gene allele having a phenotype detectable by a sensor, and (ii) a regulatory region of a preselected gene allele responsive to at least one parameter or condition of said plant or its environment, said regulatory region is operably linked to said reporter gene, such that the expression of said reporter gene phenotype is correlated with the status of said at least one parameter or condition of said biosensor plant or its environment; (b) acquiring image data of a target area comprising a plurality of said biosensor plants via said sensor and processing said data to generate a signal indicative of the phenotypic expression of said reporter gene allele of said biosensor plant; and (c) communicating said signal to an execution unit communicably linked to the sensor, said execution unit is capable of exerting in real time a selective monitoring and / or treatment of said target area or a portion thereof comprising said biosensor plants, said treatment is being responsive to said status of said parameter or condition of the biosensor plant or its environment. The present invention further discloses systems and plants related to the aforementioned method.

The invention discloses a color correction method, system and device based on cooperative flight of double unmanned aerial vehicles, a medium and a product, and relates to the technical field of agricultural detection. According to the method, a double-unmanned-aerial-vehicle cooperative flight mode is adopted, one unmanned aerial vehicle (namely the first unmanned aerial vehicle) flies at a high height for aerial photographing, the other unmanned aerial vehicle (namely the second unmanned aerial vehicle) carries a color card to synchronously fly at a low height, multiple farmland images containing the color card are obtained, a color correction matrix is constructed based on the RGB value of the color card in the farmland images and the actual RGB value of the color card, and the color correction matrix is obtained. Color correction under a dynamic illumination condition is realized, the problem of color distortion in a low-altitude remote sensing imaging process of the unmanned aerial vehicle is overcome, the reusability of cross-sensor data is improved, accurate acquisition of crop colors by the unmanned aerial vehicle in a field environment is realized, and reliable data support is provided for accurate crop management.

A crop management system including at least one crop monitoring subsystem including at least one crop sensor assembly for sensing at least one crop growth parameter in a predetermined region, at least one field monitoring subsystem including at least one field sensor assembly for sensing at least one field parameter in the predetermined region, an analysis engine receiving an output from at least one of the at least one crop monitoring subsystem and the at least one field monitoring subsystem and being operative to identify at least one anomaly in at least one of the parameters and an anomaly locator operative to provide an output indication of spatial coordinates of at least one location of the at least one anomaly.

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.

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. A trained mushroom bed model is used to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed at the plurality of times. Crop management equipment is controlled to perform a crop management program comprising a sequence of actions to be performed by crop management equipment comprising, for each current action in the sequence of actions, selecting, based on corresponding a current mushroom bed state vector, a selected crop management tool from a plurality of crop management tools. The crop management equipment is controlled to use the selected crop management tool to perform the current action on the mushroom bed.

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.

Embodiments provided herein include systems and methods for controlling crop management equipment. One embodiment includes providing a yield goal for a field from a grower, receiving tissue samples for at least one crop in the field and a seed population for the field, and interpolating individual field grid point values of the field. Some embodiments include predicting a growth plan of water and nutrient applications to meet the yield goal, based on determined sufficiency levels and individual field grid point values of the field, predicting whether the growth plan includes a component that is unlikely to be met, and in response to predicting that the growth plan includes the component that is unlikely to be met, providing the component that is unlikely to be met to a user. Some embodiments include providing an option for the grower to alter the yield goal such that the component is likely to be met.

The invention relates to the technical field of crop management, and discloses a yield prediction method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining the historical yield of crops in the historical time of a target region range and the historical parameter value of a yield influence factor; constructing a time sequence knowledge graph among the yield, the influence factors and the time; taking the time sequence knowledge graph as the input of a yield time sequence prediction model, and obtaining the estimated yield of crops in the target region range; and performing interpretable analysis on a prediction result of the yield time sequence prediction model based on an interpretable model, and determining the influence degree of the yield influence factors on the estimated yield. According to the method, the time sequence knowledge graph is constructed, and model yield prediction and interpretable analysis are carried out, so that influence factor data in continuous time can be effectively associated and fused, a dynamic coupling relationship among multiple influence factors is disclosed, the yield prediction accuracy is improved, reliable data reference and scientific guidance are provided for technicians, and the method is suitable for popularization and application. The wheat yield is further improved.

The invention provides a corn phenology remote sensing monitoring method based on NIRv and a crop progress report. Comprising the following steps: constructing an NIRv time sequence curve by utilizing remote sensing data; constructing a corn growth model in combination with the open crop progress report and the cultivated land utilization data; calibrating phenological parameters by using data in previous years and combining with the growth model to obtain a shape model; a traditional SMF method is optimized, a shape model and an annual vegetation index time sequence curve are fitted, optimal parameters are calculated, and five main phenological periods of corn are monitored; pixel-level corn phenology monitoring information is obtained, regional phenology mapping is carried out, and quantitative evaluation is carried out on regional phenology. According to the method, the modeling problem that accurate estimation of the phenological period of the crop can be achieved without depending on long-term observation data on the ground is solved, the application range of shape model fitting in phenological monitoring is expanded, the phenological monitoring capacity of the NIRv index is evaluated, and the method is suitable for research in a large area. The remote sensing monitoring precision of crop phenology is further improved, and governments and farmers are assisted in accurately and scientifically managing crops.

The invention relates to the technical field of field character detection, in particular to a crop breeding-based field character detection method, which comprises the following steps of: determining an environment penalty value according to detected color data and soil data of field crops, determining a yield reward value according to the detected field crop growth data and the nutrition data stored in the crop management platform, and determining a fertilizer utilization value according to the 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 a crop management platform; the fertilization amount reinforcement learning model is updated regularly based on the fertilization adjustment reward function, and the fertilization amount reinforcement learning model determines the breeding multi-element recommended fertilization amount based on the Critic network and the Actor network. According to the method, collaborative closed-loop detection and field detection data mining of stress resistance, yield increase and fertilizer saving of the reinforcement learning model in combination with breeding are realized.

The utility model provides a final drive assembly for increasing ground clearance and a high ground clearance tractor, the final drive assembly for increasing ground clearance comprises a gear box, two opposite sides of the gear box are provided with an output disc and a half shaft shell, the lower part of the gear box is provided with an output shaft, and the output shaft is connected with the output disc; one end of the half shaft shell is fixed to the upper portion of the gearbox, an input half shaft and a transmission shaft are arranged in the half shaft shell, the input half shaft is in transmission connection with the opposite end of the transmission shaft, the other end of the transmission shaft extends into the gearbox and is in transmission connection with an output shaft of the gearbox, and the other end of the input half shaft extends out of the half shaft shell. According to the utility model, the power of the input half shaft is transmitted to the output disc through the gear box, so that the transmission of the walking power of the tractor is realized; the output disc is lower than the input half shaft, so that the ground clearance of the tractor is increased, the tractor can cross higher crops to perform crop management operation, and the chassis of the tractor is prevented from damaging the crops.

The disclosure relates to methods and related systems for precision crop modeling and management using the same. Precision crop modeling and management can be incorporated into various methods for growing plants such as crop plants and various methods for managing the growth of such plants in a particular field. The methods generally utilize in-season information relating to weather conditions actually experienced by the field to prepare mid-season, updated crop management plans. A crop management plan is determined using a crop model incorporating a variety of inputs and plant-specific material and energy balances to specify one or more grower-controlled management parameters. An updated plan for a given field can be followed by a grower to increase crop yield and / or optimize one or more other crop or field parameters.

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.