151 results about "Row crop" patented technology

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

The invention relates to the technical field of water conservancy monitoring, and provides an intelligent water conservancy monitoring method for an irrigation area and related equipment thereof. Water body boundary extraction is carried out on remote sensing image data to obtain an initial water body boundary data set, time sequence interpolation processing is carried out on the initial water body boundary data set to obtain a water body change trend data set, and soil moisture content prediction deduction is carried out according to a satellite cloud picture and the water body change trend data set to generate a soil moisture content prediction data set. Performing water level and area fitting on the regional DEM according to the water body change trend data set to construct a water storage capacity model, and performing crop water demand calculation according to regional data and crop data to obtain a crop water demand matrix data set; and performing constrained programming according to the soil moisture content prediction data set, the crop water demand matrix data set and the water storage capacity model to obtain a multi-target irrigation scheduling decision set. According to the application, dynamic balance between irrigation requirements and water resource supply is realized through multi-level and multi-source data fusion and intelligent scheduling.

The invention discloses an inter-ridge autonomous navigation method and system for an asparagus picking robot based on machine vision, and belongs to the technical field of agricultural automation. The method comprises the following steps: processing a field image acquired by a robot in real time by using a pre-trained lightweight inter-ridge path segmentation network to generate a path mask; extracting and smoothing left and right boundary points of the path based on the mask; fitting a middle navigation line; and finally, a control error is calculated according to the navigation line and the robot pose, and a steering instruction is generated to drive the robot to autonomously travel. Through lightweight model design and a robust path fitting algorithm, the problems that an existing visual navigation method is large in calculation amount, poor in real-time performance and insufficient in precision are solved, an efficient, accurate and easy-to-deploy autonomous navigation solution is provided for an agricultural robot, and the method is particularly suitable for complex field environments of ridge crops such as asparagus.

The invention relates to the field of agricultural planting, and discloses a water and fertilizer pesticide spraying integrated high-altitude spraying system which comprises a sensing unit used for obtaining crop physiological and morphological information, dynamic environment field information and physical property information of mixed liquid; the decision-making and optimizing unit is used for comprehensively diagnosing the crop growth condition, generating a water-fertilizer-pesticide mixed formula, predicting a spraying track and optimizing spraying parameters according to the information acquired by the sensing unit; the execution unit is used for executing spraying operation according to the spraying parameter optimization result; and the feedback and calibration unit is used for monitoring the spraying operation effect in real time and providing calibration information. According to the invention, the density and the dynamic viscosity of the mixed liquid are integrated into the three-dimensional motion physical model of the fog drops, so that the system can dynamically adjust the actual physical characteristics of the water-fertilizer-pesticide mixed liquid in different proportions and at different temperatures, the accuracy of mist spraying track prediction is further improved, and invalid spraying is reduced.

The invention provides a greenhouse environment control method and system based on an AI algorithm, and the method comprises the following steps: collecting environment data in a greenhouse through environment collection equipment, and carrying out the preprocessing of the environment data, and generating primary data; transmitting the primary data set to a cloud computing platform, randomly splitting the primary data set into a plurality of contrast data sets through the cloud computing platform, and operating a crop growth model on the cloud computing platform in combination with weather forecast and the plurality of contrast groups so as to respectively obtain plant growth prediction data; inputting the plant growth prediction data into an adjustment model for analysis so as to output a plurality of corresponding pre-adjustment parameters; and performing overall calculation on the plurality of pre-adjustment parameters to obtain a target adjustment parameter, and adjusting the regulation and control equipment in the greenhouse according to the target adjustment parameter. According to the invention, the environment in the greenhouse can be automatically controlled, and the growth efficiency of plants in the greenhouse is improved.

The invention discloses a farmland segmentation and crop identification method based on cross-granularity remote sensing fusion, and belongs to the technical field of agricultural remote sensing and intelligent farmland management. The method comprises the following steps: firstly, acquiring and preprocessing a high-resolution fine-grained image and a multi-temporal medium-low-resolution coarse-grained image of a target area; performing over-segmentation by using the fine-grained image to generate a super-pixel object and constructing a graph structure model; extracting a core region through morphological corrosion to form a core object, and distinguishing a farmland region from a non-farmland region; taking the kernel object as a unit, and extracting a multi-dimensional feature set covering a crop growth cycle from the coarse-grained image; carrying out crop type identification by adopting a machine learning classifier based on the kernel object features; and finally, mapping the identification tag back to the super-pixel object, and generating a plot level classification map with accurate plot boundary and crop category information after boundary optimization. According to the method, the advantages of remote sensing data with different spatial resolutions are fused, so that the comprehensive precision and practicability of plot segmentation and crop identification in a complex farmland area are effectively improved.

The invention discloses a crop planting scheme generation method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the natural language understanding of to-be-processed agricultural information, obtaining language understanding information, and converting the language understanding information into an information vector; constructing an agricultural knowledge graph based on the to-be-processed agricultural information; fusing the information vector and the agricultural knowledge graph to obtain a fusion result, and determining a retrieval result which corresponds to the agricultural knowledge graph and comprises a target entity and entity relationship path in the agricultural knowledge graph by utilizing a knowledge retrieval enhancement technology and based on the fusion result; determining a target context based on the retrieval result and an initial context corresponding to the large language model, and generating a crop planting scheme for the target context by using the large language model to obtain a target crop planting scheme; the target crop planting scheme comprises the explanation information corresponding to the planting suggestions, so that the efficiency of generating the crop planting scheme is improved.

The invention relates to the technical field of agricultural machinery, and provides an open field vegetable swing type intelligent inter-plant weeding device and a control method thereof, and the device comprises a plurality of weeding monomers, a sensor mechanism and a control mechanism. The sensor mechanism comprises depth cameras, laser sensors and absolute value encoders, the depth cameras are installed on the front portions of the multiple weeding single bodies, the visual field covers multiple rows of crops, and the laser sensors and the absolute value encoders are arranged on the weeding single bodies; the control mechanism is connected with the depth camera, the laser sensor and the absolute value encoder and used for processing data of the sensor mechanism, calculating the fusion height through a fusion algorithm and controlling and driving the lifting mechanism, the transverse moving mechanism and the weeding knife opening and closing mechanism to act based on the error between the fusion height and the target depth so as to achieve accurate weeding and seedling avoiding. The device can quickly avoid crops while effectively removing weeds between rows and plants, and finally realizes mechanical weed removal of open field vegetable crops which are large in bare soil proportion and easy to generate weeds.

The invention provides an unmanned aerial vehicle variable fertilization method and system fused with multi-modal data, which is applied to the technical field of smart agriculture and comprises the following steps: acquiring time sequence multi-modal data of a target crop area; constructing a multi-scale three-dimensional growth model of the target crop region based on the time sequence multi-modal data; according to multi-modal deep learning, crop core leaf positioning is carried out based on a multi-scale three-dimensional growth model, and core leaf center point coordinates and crop ridge core leaf center lines are obtained; according to a fluid mechanics-discrete element-finite element coupling mode, a multi-scale three-dimensional growth model and the coordinates of the center points of the core leaves of each crop are used for simulating the movement track of fertilizer particles, and the fertilization safety distance is determined; based on the crop ridge core leaf center line of each crop, the fertilization safety distance in different growth periods and a preset fertilization amount, generating fertilization safety area thermodynamic diagrams of the crops in the target crop area in different growth periods, and planning an unmanned aerial vehicle path; and precise fertilization operation is carried out. According to the invention, the core-burning-free precise fertilization of the unmanned aerial vehicle can be realized.

Automated precision applicator for row crops with dual sided applicators that can be used to direct pollen to multiple areas of the plant, as well as to measure phenotypic characteristics of plant fruiting bodies and detect successful pollination.

An agricultural machine for arrangement on a vehicle, wherein during movement of the machine in the travel direction an area of soil at least between plants arranged parallel to the travel direction in row crops can be tilled by the machine, wherein the machine comprises a soil tillage tool, which is pivotable back, counter to the travel direction, about an axis of rotation running in a height direction of the machine out of a transverse direction arranged perpendicular to the travel direction and perpendicular to the height direction and / or is movable towards the machine in the transverse direction, an obstacle sensor by which obstacles can be detected, and comprises a control device, wherein when an obstacle is detected the control device can pivot the soil tillage tool back out of the transverse direction counter to the travel direction and / or move it in the transverse direction towards the machine.

This invention relates to a spraying device for defoliants on edible sunflowers and its usage method. The device includes a drone body with a liquid tank in the center. An annular plate is vertically movable along the outer wall of the drone body, and a windbreak plate is slidably connected to the side wall of the annular plate. A first spray pipe is fixedly mounted at the center of the bottom of the drone body. Second spray pipes are rotatably connected to both ends of the first spray pipe, and the other end of each second spray pipe is rotatably connected to vertical plates on both sides of the bottom of the drone body. The rotation of the second spray pipes is synchronized with the raising and lowering of the windbreak plate. Atomizing nozzles are fixedly mounted at the bottom of both the first and second spray pipes. This invention forms a vertical air curtain through the windbreak plate, providing a stable falling environment for the droplets and effectively preventing drift. The inclined spraying of the atomizing nozzles on both sides makes the liquid distribution more uniform and widens the effective coverage area, ensuring that the entire row of crops receives sufficient liquid from the center to the edge, eliminating missed spraying along the row edges, and improving the spraying effect.

According to an aspect, there is provided a systems and methods for assessing crops. The crop assessment includes, for each given plant of a plurality of plants, assigning a unique identifier to the given plant, emitting light toward the given plant using at least one light emitter, capturing an image using at least one imaging sensor, detecting the given plant and a predictor of a crop output of the plant in the image, estimating a location of the given plant based on a signal from a geo-positioning subsystem, and generating at least one crop metric predictive of yield of the given plant based on the crop output, and generating a crop output data structure with data defining the at least one crop metric and the location, in association with the unique identifier, for each of the plurality of plants.

A method for farmland path recognition and planning based on aerial imagery is proposed. First, a data set (DOM) is constructed, cropped, and scale-normalized to form a dataset for recognition and planning. Next, multi-scale identification of the inter-row crop passage area is performed to obtain passable areas. Then, path planning is performed based on the inter-row structure: the centerline of the passable area is extracted to obtain a skeleton; principal direction constraints and structural consistency analysis are applied to the skeleton; branch removal and break repair are performed to obtain continuous, directional, and consistent inter-row centerline paths. The paths are clustered, grouped, and sorted to automatically distinguish different ridges and determine the walking order within the plot. Based on the endpoint relationships of adjacent inter-row paths, cross-row connection paths are generated at the ridge ends, forming a global navigation path combining straight and turning routes, and determining the entrance and exit points. Finally, the results are converted into standard path data for output, providing navigation for ground-based equipment. This invention effectively improves the stability and consistency of planning.

The invention discloses a seedling condition information determination method and device, equipment and a storage medium. The method comprises the steps of performing conventional segmentation on an acquired unmanned aerial vehicle image to obtain a conventional segmented image set, and performing dislocation segmentation on the acquired unmanned aerial vehicle image to obtain a dislocation segmented image set; determining a first image set according to the conventional segmentation image set, and determining a second image set according to the error segmentation image set; according to a pre-trained crop seedling identification model, identifying the first image set to obtain a first seedling identification result, and identifying the second image set to obtain a second seedling identification result; and determining seedling condition information according to the first seedling identification result and the second seedling identification result. According to the technical scheme of the embodiment, the crop seedling identification model is pre-trained, the two segmented images of the unmanned aerial vehicle image data are identified, the seedling condition information of the target crop field row crops is determined, and crop field seedling condition monitoring based on the unmanned aerial vehicle image is realized.

A row crop header for harvesting crop in a field comprises a header frame having a center section and a pair of side wing sections operatively coupled to the center section. An upper link is pivotally coupled between the center section and each one of the side wing sections adjacent the top portion thereof. A lower link is pivotally coupled between the center section and each one of the side wing sections adjacent the bottom portion thereof. The upper links and lower links provide independent pivotal movement of the side wing sections relative to the center section to contour to the surface of the field of crops to be harvested. An automatic float system is operatively coupled between the center section and each one of the side wing sections to adjust the weight of the side wing sections on the surface of the field and allow the side wing sections to float relative to the center section.

The invention discloses a method for improving the quality of low-yield slope cropland in mountainous and hilly areas, and belongs to the technical field of cropland quality improvement and improvement. (2) plot planning: performing plot planning according to topographic elements; (3) digging, filling and leveling: performing slope reduction, soil improvement and leveling on the slope cropland according to plot planning; and (4) soil fertilization: after the land parcel treatment is completed, soil fertilization is performed, crop planting is started, the combined operation of organic fertilizer, straw returning to the field and crop planting is adopted for soil fertilization for more than or equal to 3 years, the quality improvement of the low-yield slope cropland in the mountainous and hilly areas is completed, and the problems that at present, the slope cropland is poor in soil physicochemical property, the improvement effect is repeated, and the high-quality land parcel is difficult to continue are solved.

The invention relates to a crop row detection and row control method for a high-ground-clearance plant protection machine, and the method comprises the following steps: S1, collecting a crop row image in a working scene of the high-ground-clearance plant protection machine, and carrying out the data enhancement processing and marking; s2, constructing a crop row detection model and performing training; in the crop row detection model, a C2fCIBPConv module integrating partial convolution PConv and point-by-point convolution PWConv is used for replacing a C2fCIB module in the original YOLOv10n model, and an EMA attention mechanism module is embedded between a backbone network and a neck network; s3, deploying the trained crop row detection model on a high-ground-clearance plant protection machine, and carrying out crop row identification on images collected in real time; and S4, calculating a transverse deviation and a course angle deviation between the plant protection machine and the crop row based on an identification result, generating a row control instruction through a control algorithm combining adaptive PID with feedforward compensation, and driving a steering system to realize dynamic row control. According to the invention, high-speed and high-precision detection of crop rows can be realized, and row alignment deviation is stabilized within + / -5cm.

The invention belongs to the technical field of remote sensing data assimilation, and particularly relates to a 4DVar assimilation yield estimation method based on WOFOST model and SAR remote sensing data fusion. In order to improve the accuracy and reliability of crop yield prediction, the method comprises the following steps: obtaining crop actual measurement data, and carrying out WOFOST crop model sensitivity analysis and localization standard; sAR remote sensing data are acquired, and a total photosynthetic area index (TPAI) is inverted based on polarization decomposition and a machine learning algorithm; constructing a 4DVar assimilation model, and dynamically adjusting model parameters by using a variational principle in a time window so as to minimize the difference between a model simulation value and a remote sensing inversion value in a cost function; and an SCE-UA global optimization algorithm is introduced, and high-dimensional parameter-state joint optimization is realized through population division and competitive evolution mechanism iterative optimization.

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.

The invention provides a crop disease identification method based on multi-modal fusion and knowledge distillation in the technical field of crop disease identification, and the method comprises the steps: S1, collecting a large amount of historical multi-modal crop monitoring data to construct a data set, and carrying out the time dimension self-adaptive matching of the data set; s2, creating a crop disease recognition model based on a feature extraction module, a cross-modal interaction module and a prediction output module, training the crop disease recognition model through the data set, and taking the trained crop disease recognition model as a teacher model; s3, performing knowledge distillation on the teacher model so as to transfer knowledge learned by the teacher model to the created student model, and deploying the student model to the edge computing device; and S4, the edge computing device carries out crop disease identification based on the deployed student model. The method has the advantages that the disease identification precision is greatly improved, the generalization ability is enhanced, and the feasibility and efficiency of actual deployment are considered.

The present invention relates to the technical field of agricultural machinery, and discloses a directional weeding partition device for soybean and corn strip planting, comprising a base plate, both sides of which are rotatably connected to travel wheels, a medicine storage barrel fixedly installed on the top of the base plate, a support protection cylinder fixedly connected to the front of the medicine storage barrel, a support sleeve fixedly connected to the top of the support protection cylinder, and the base plate is divided into two independent barrels for storing weeding liquids suitable for two different crops, and the support protection cylinder and the support sleeve are connected in a "T" shape. The present invention arranges a telescopic rod so that the distance between the partitions can be flexibly adjusted from fixed to adapt to the fields with the same planting spacing; and uses the water pressure applied by the water pump when spraying the pesticide to control the extension length of the telescopic rod, so that the partition device can be adjusted synchronously with the spraying range; and controls the inward rotation of the partition plate by periodically closing the spray pipe to reduce pesticide waste and avoid contamination of inter-row crops.

The invention relates to a hoeing machine (1) for row crops (100) comprising a frame assembly (2) attachable to a tractor for towing along a direction of travel (90) parallel to a horizontal longitudinal axis (91), wherein a horizontal transverse axis (92) and a vertical axis (93) are defined perpendicular to the longitudinal axis (91), and at least one working unit (3) arranged on the frame assembly (2) for soil cultivation between the row crops (100), wherein each working unit (3) comprises: two disc units (4) each with a disc (41) and a drive unit (42), wherein each drive unit (42) is designed to rotate the respective disc (41) about a disc rotation axis (43), wherein the two discs (41) are horizontally aligned for undercutting the soil, an adjustment mechanism (5),which is designed to adjust a distance (51) defined parallel to the transverse axis (92) between the two disk rotation axes (43).

In one aspect, the present subject matter is directed to a system which includes a row crop mitigation system configured to mitigate damage to a row crop by changing a steer angle of a ground engaging element of an agricultural work vehicle. The row crop mitigation system includes a crop row sensor configured to generate crop plant data, as well as a mitigation steering controller configured to selectively change the steer angle of the ground engaging element of the agricultural work vehicle. The mitigation steering controller having an inactive state in which the mitigation steering controller does not change a steer angle of the ground engaging element, and an active state in which the mitigation steering controller is configured to generate a mitigation steer angle command to change the steer angle of the ground engaging element.

The present disclosure provides a cultivating method for promoting productivity of a crop, which comprises the following steps: step (a): providing a fermented solution of Bacillus subtilis natto, wherein the fermented solution of Bacillus subtilis natto is obtained by placing a Bacillus subtilis natto strain into a fermentation medium and then conducting fermentation; and step (b): applying the fermented solution of Bacillus subtilis natto to a cultivated field of a crop, and then conducting cultivation of the crop. The productivity of the crop obtained by the cultivating method of the present disclosure is obviously.

The invention relates to the technical field of agricultural intellectualization, discloses a crop yield prediction method based on whole-course mechanized operation data, and aims to solve the problem of inaccurate prediction of crop yield prediction simply depending on experience and initial conditions in the prior art. According to the method, the operation data are collected when the crops perform operation actions in different growth periods, the feature data are extracted from the operation data, and the feature data extracted in previous operation links are combined and input into the yield prediction model for yield prediction, so that dynamic yield prediction according to different operation links is realized. By adopting the technical scheme, the dynamic change of crop growth is considered, different types of operation data are collected in different operation links, and the characteristic data capable of representing the crop yield is extracted from the operation data to perform yield prediction, so that the yield prediction can be realized in different growth cycles of crops, and the yield prediction efficiency is improved. And comprehensive yield prediction is carried out in combination with data acquired in previous operation links, so that the prediction accuracy is improved.

This invention provides a method for identifying trampling-resistant varieties of regenerated crops based on UAV lidar point clouds. The method includes: acquiring three-dimensional point cloud data of a target monitoring area from multi-temporal global images of crops collected by a UAV lidar device; extracting trampled and untrapped row crop points from the three-dimensional point cloud data; extracting trampling resistance indices from the trampled and untrapped row crop points respectively; predicting the trampling resistance of the crop based on the trampling resistance indices corresponding to the trampled and untrapped row crop points; and determining whether the crop is a trampling-resistant variety based on its trampling resistance. This invention enables high-throughput automatic identification of trampling-resistant varieties of regenerated crops, which is beneficial for improving the screening efficiency of high-yielding varieties of regenerated crops.

The disclosed apparatus, systems and methods relate to a physical stalk sensing system comprising at least one resilient member. Sensors having the resilient member or members are able to estimate the size of the stalks of row crops as they pass through a field, such as a corn field. The sensors can be mounted on a corn head and the results can be analyzed and visualized. A system for predicting crop yields in real-time or near real-time. A system for adjusting a stripper plate gap.

The application discloses a cross-region crop remote sensing image processing method, device and equipment fused with geographic information, relates to the technical field of crop data processing and remote sensing cross technology, constructs a generalization data set of latitude and terrain joint partitioning through sample geographic information data and sample satellite remote sensing image data, obtains a geographic prototype library corresponding to a geographic unit, then trains a crop classification prediction model based on the geographic prototype library, and performs crop classification prediction by using the crop classification prediction model, which can effectively capture the combined influence of geographic multidimensional factors on crop growth, solve the fundamental problem of poor generalization of the cross-region model, improve the cross-geographic processing capability of the crop classification prediction model, and realize high-precision, high-robustness and low-cost crop remote sensing classification.