72 results about "Crop management" patented technology

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.

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.

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.

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.

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 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.

The present invention relates to the technical field of crop management, and more specifically to a method for managing crops on red soil slopes. The method involves obtaining crop data, surrounding environment data, and soil data corresponding to crops planted in a target red soil slope area; identifying the crop data and soil data to determine the current drought level corresponding to the crops; identifying the crop data to determine the current pest and disease type and current pest and disease level corresponding to the crops; determining the cause of the drought level and the cause of the pest and disease level corresponding to the crops based on the surrounding environment data and soil data; outputting an irrigation strategy based on the current drought level and the cause of the drought level; and outputting pest and disease control measures corresponding to the crops based on the current pest and disease type, current pest and disease level, and the cause of the current pest and disease level. This method achieves comprehensive, scientific, and accurate management of crops on red soil slopes, improves efficiency, and saves time and labor costs.

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 potion 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 present disclosure discloses a method for evaluating the nutritional status in plants, using visible-to-shortwave (VIS-SWIR) infrared reflectance spectroscopy of carbohydrates, particularly in almond trees. The method comprises obtaining a dry plant sample (i.e. leaves, branches and roots), digesting and grinding it to a powder, capturing predetermined spectral data of the ground plant sample, correlating said spectral data to predetermined materials and evaluating said nitrogen status of said plants according to predetermined multivariate statistical models. The data obtained from this method facilitates crop management and fertilization by providing the nitrogen status of the plants based on non-structural carbohydrates.

This invention provides a seedling planter, belonging to the field of agricultural technology. It solves the problem of high manpower, material, and financial costs and low planting efficiency in vegetable cultivation in my country. The seedling planter includes a frame, a tray feeding device, a seedling grabbing device, a rotary seeding device, and a transmission device. The tray feeding device includes a tray placed at the top, which intermittently transports seedlings placed in the tray forward. The seedling grabbing device is located above the tray feeding device and can grab seedlings from the tray at the end of the seedling transport. The rotary seeding device can catch seedlings that slide off the seedling grabbing device and plant them in the soil. The transmission device provides power to the entire device. It has the advantages of improving planting efficiency, reducing labor intensity, reducing labor costs, saving a large number of seedlings, facilitating crop management and harvesting, and increasing yield.

The invention discloses a bending-over type crop intelligent multifunctional management platform and a working method thereof, and relates to the field of agricultural robots. The intelligent multifunctional crop management platform comprises an electric four-wheel-drive chassis walking system, an intelligent human body comfortable bending control system and a fruit basket auxiliary transferring system. Through an intelligent voice physical fusion control method, convenient control over the advancing speed, steering, bed bending angles and physical sudden stop in emergency situations of the electric four-wheel drive chassis is achieved; through a crop inter-row autonomous walking method, autonomous navigation of the management platform along rows is guided, and human intervention in management operation is reduced; operation jolt and bearing pressure in a complex field environment are reduced through a head bearing self-adaptive adjusting method; through an operation blind area voice regulation and control method, the working visual field of workers in prone operation is expanded. The method has a remarkable promoting effect on reducing labor force and improving economic benefits in agricultural production.

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 invention relates to the technical field of crop management, in particular to a red soil slope crop management method. Acquiring crop data, surrounding environment data and soil data corresponding to crops planted in the target red soil slope area; identifying the crop data and the soil data, and determining a current drought level corresponding to the crops; identifying the crop data, and determining a current insect disease type and a current insect disease grade corresponding to the crops; determining a drought grade reason and an insect disease grade reason corresponding to the crops based on the surrounding environment data and the soil data; outputting an irrigation strategy based on the current drought level and the drought level reason; based on the current pest disease type, the current pest disease grade and the current pest disease grade reason, outputting pest control measures corresponding to the crops. The method achieves the comprehensive, scientific and accurate management of the crops in the red soil slope, improves the efficiency, and saves the time cost and manpower cost.

The invention discloses a tea tree crown accurate cultivation method based on environment-image data fusion, and relates to the technical field of crop fine management, the method comprises the following steps: multi-source data acquisition: respectively obtaining topographic data, environment data and image data of a tea garden; data fusion calculation: calculating a terrain turbulence enhancement coefficient based on the obtained terrain data and environmental data; establishing a canopy transpiration mechanism: setting a trigger condition through the terrain turbulence enhancement coefficient and the environmental data, and dynamically correcting the canopy transpiration when the trigger condition is reached; and determining a regulation and control strategy: combining the terrain turbulence enhancement coefficient and the corrected canopy transpiration with the image data, and generating the regulation and control strategy. Through fusion processing of multi-dimensional information such as environment monitoring, infrared images and visible light images, a regulation and control feedback chain is established, automatic circulation of sensing, judgment, execution and feedback is achieved, and intelligence and real-time performance of canopy structure regulation and control are improved.

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.

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 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.