57 results about "Crop management" patented technology

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 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.

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.

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 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.

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 invention discloses a soil attribute and crop character prediction and early warning method based on a multi-task Transformer model guided by soil knowledge, the method provides a KGMT guided by soil knowledge, the KGMT model combines deep learning and multi-task learning, the hyperspectral image of an unmanned aerial vehicle is directly used as input, and the deep learning and the multi-task learning are combined. And complex artificial feature extraction is avoided. The model can predict multiple soil properties and crop traits at the same time, and capture complex mutual relations which are often neglected by a traditional method. The invention develops a set of soil early warning method combining bare soil stage soil properties and growth stage crop properties, realizes early recognition of potential crop growth limitation, provides an active strategy for soil and crop management, and provides an accurate agricultural practice by providing an operable insight of soil-crop interaction and a KGMT model. Soil conditions can be optimized, and crop yield and sustainability can be improved.

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.

Crop modeling methods can be used for mapping temporal and spatial stability and sustainability of a cropping system. In some methods, remote sensing imagery over multiple time series elements (such as for past growing seasons) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, remote sensing imagery over multiple time series elements (such as for the growing season) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, a crop model is used to determine dependent cropping system parameters related to agricultural sustainability, which can be used to characterize small-scale field sustainability scores for such parameters relative to larger surrounding land areas. Such stability and sustainability maps can inform crop management activities for fields in the larger land areas and on the smaller scales, for example using crop models to determine such crop management activities to improve crop productivity, improve economic productivity, and / or reduce adverse environmental impact for the field as a whole and / or sub-regions thereof.

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.