111 results about "Row crop" patented technology

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 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.

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.

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.

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 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 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).

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.

The present application relates to a kind of unmanned aerial vehicle image radiation consistency correction method under varying illumination conditions, comprising: obtaining the orthographic image of target farmland area;Orthographic image of target farmland area is positioned and corrected in space, and the spatial position of each farmland plot or test plot in orthographic image in target farmland area is obtained;According to the spatial position of each farmland plot or test plot in orthographic image, orthographic image is cut, and the radiation information affected by illumination change in cut image is corrected in radiation consistency;Based on the image data after radiation consistency correction, crop growth monitoring, farmland condition evaluation, variety comparison analysis or yield prediction are carried out.The present application can be widely applied to the field of remote sensing image processing and spectral correction.

The application relates to the technical field of agricultural picking, in particular to an intelligent picking and collecting system and a control method thereof. The intelligent picking and collecting system comprises a collecting mechanism, a controller, an executing mechanism and a clamping mechanism in communication connection with the controller, wherein the controller is used for controlling the executing mechanism, the clamping mechanism and the collecting mechanism; the executing mechanism is used for adjusting the pose of the clamping mechanism under the control of the controller; the clamping mechanism is used for picking crops under the control of the controller; and the collecting device is used for collecting the crops picked by the clamping mechanism. The depth camera arranged on the clamping mechanism is used for realizing the identification and classification of the objects to be picked; and the object bowl added to the clamping mechanism is used for conveniently and quickly storing the picked objects.

A kind of construction method of agricultural navigation map and application, the construction method of agricultural navigation map, with latitude and longitude as center, while superimposed crop information generates agricultural fine navigation map, for other vehicle reference travel, including the following steps: agricultural robot adopts vision navigation along crop row of crop to carry out crop row detection, image is collected and after processing obtains image center line and crop row transverse and steering error;Image center line and crop row transverse and steering error are input into MPC controller to follow crop row straight line, record the latitude and longitude coordinates of agricultural robot every time in the process of walking;And to the latitude and longitude coordinates recorded are fitted, output the satellite coordinates after processing and generate agricultural fine navigation map.The application of the agricultural navigation map is also provided.The latitude and longitude of the crop row information introduced in the application generate driving route, which greatly avoids the damage of autonomous vehicle to crops.

The application discloses an irrigation optimization method based on a crop model, and comprises the following steps: acquiring input data such as meteorological data, soil data and crop data; running a crop growth model to simulate crop growth and water and salt dynamics under an irrigation scheme, wherein the model dynamically calculates root water absorption stress according to calculated crop growth stage indexes; and based on simulation results of the model and according to multiple optimization targets such as water saving, yield maintenance and salt control, an optimal irrigation scheme is obtained by solving the multiple optimization targets by using a multi-objective optimization algorithm. By introducing a stress calculation mechanism that changes with the growth stage, the application improves the simulation accuracy, and can efficiently solve the problem in combination with a proxy model, so that the obtained irrigation scheme is more reliable and practical in balancing the targets.

The invention discloses an environment adjusting method for soil of a black soil root system layer, and belongs to the technical field of facility agriculture. The adjusting method comprises the steps that the depth range of the black soil root system layer is obtained after ploughing, and the black soil caking state in the depth range is obtained; based on the black soil caking state, preparing a black soil caking modifier; based on a black soil caking modifier dispenser, a black soil caking modifier is injected into the soil of the black soil root system layer; a nutrient dispenser is arranged on the black soil root system layer, and base fertilizer is dispensed; monitoring a crop state in a crop growth period in real time, and determining a nutrient demand of the crop; based on root system distribution of crops, nutrients are put into different root system layer depths; monitoring the caking state of the root system layer of the black soil during the growth period of the crops, and putting the black soil caking modifier during the growth period of the crops. The problem that the black soil root system layer is difficult to efficiently and individually adjust in the prior art is solved, and then the environment adjustment effectiveness of the black soil root system layer is fully improved.

The application provides a crop organ segmentation method and device based on semi-supervised learning. The first image sample of the labeled crop organ is used to train a semantic segmentation network, the obtained first training result is compared with the labeled crop organ, the semantic segmentation network is parameter adjusted, and a segmentation baseline model capable of crop organ segmentation is obtained. The unlabeled image of the obtained unlabeled crop organ is predicted and segmented to determine a crop organ prediction mask, the unlabeled image is screened according to the crop organ prediction mask, and the second image sample of the unlabeled image is obtained. The first image sample and the second image sample are used to semi-supervisedly train a semi-supervised model based on guided distillation, and then a segmentation model is obtained. Without labeling all samples, the segmentation model capable of accurately segmenting crop organs can be obtained. Finally, the segmentation model is used for segmentation processing on the crop image to be processed, and then an accurate crop organ segmentation mask is obtained.

The invention relates to the technical field related to agricultural management, in particular to an agricultural planting scheme dynamic optimization method and system based on a knowledge graph, and the method comprises the steps: drawing up a planting topological graph, combining a soil fertility level with a resource consumption coefficient, determining density and water and fertilizer matching parameters, and determining first and second deviation compensation data; and configuring a distributed planting optimization architecture, and carrying out dynamic optimization on an agricultural planting scheme. The technical problems of poor crop growth consistency and low resource utilization efficiency caused by static solidification of an agricultural planting scheme and lack of dynamic consideration of influence factors such as resource consumption and plant diseases and insect pests are solved, crop growth state data and a deviation compensation mechanism are introduced, real-time adaptation of the planting scheme, environment change and crop growth states is carried out, and the crop growth quality is improved. The method has the technical effects that the crop suitable growth range and the planting area nodes are accurately mapped through the planting topological graph, the crop growth consistency and the resource utilization efficiency are improved, and the reliability and the stability of planting scheme optimization are remarkably improved.

The present application belongs to the technical field of saline-alkali soil improvement, and particularly relates to a method for improving soda saline-alkali soil by using citrus orange and tangerine waste, comprising the following steps: S1, crushing the collected citrus orange and tangerine waste and performing solid-liquid separation to obtain pretreated waste liquid A and pretreated waste residue B respectively; S21, obtaining an improvement liquid by treating the pretreated waste liquid A, and irrigating the improvement liquid to the saline-alkali soil; S22, obtaining an improvement fertilizer by treating the pretreated waste residue B, and laying the improvement fertilizer on the ground of the saline-alkali soil; and S3, planting crops after rotary plowing the saline-alkali soil. The present application uses citrus orange and tangerine fruit drop and other waste as improvement material, realizes secondary utilization of agricultural waste, is easy to collect, low in cost and pollution-free, utilizes the rich organic acid therein to improve the saline-alkali soil, and is a green and efficient soil improvement method.

An apparatus, system, and method that 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 present application relates to the technical field of smart farming. Disclosed are a big-data–based smart-farming intelligent monitoring system and method. The system comprises: a farming data monitoring module, a crop growth prediction module, and an agricultural-machinery harvesting assignment module, wherein the farming data monitoring module uses remote sensing technology to monitor image information of all farmland and crops planted within a supervised region, perform grid-based partitioning on the farmland, and analyzes and calculates crop maturity; the crop growth prediction module is used for constructing a crop growth state prediction model, and predicting an estimated crop maturity of farmland crops at a future time point; and the agricultural-machinery harvesting assignment module calculates the harvesting performance of agricultural machinery in respective harvesting regions assigned thereto, and performs analysis to acquire an agricultural-machinery assignment strategy that maximizes a total harvesting performance for all farmland within the supervised region.

The invention discloses an intelligent processing method and system for agrometeorological observation data, and relates to the technical field of agrometeorological data processing, and the method comprises the following steps: S1, receiving the agrometeorological observation data, including manual input data, Internet of Things equipment data and satellite remote sensing data; s2, performing block chain evidence storage operation on the agrometeorological observation data, and generating a non-tampering record including an operator signature, a data hash chain and a timestamp; s3, constructing a three-level cleaning mechanism to verify the agrometeorological observation data; s4, mapping the verified agrometeorological observation data to a geographic grid space, and fusing multi-source data to generate a space-time continuous data set; and S5, carrying out crop growth stage analysis, disaster influence evaluation and yield prediction based on the fused data, and outputting a result. According to the method, human tampering is completely eradicated from the source through full-link trace leaving, and meanwhile, data is scientific and reliable through data cleaning and verification.

The invention relates to a suction support type high-speed precise pneumatic seed-metering device which comprises a seed-metering device shell, an air chamber shell, a seed-metering air chamber, a suction support type seed-metering disc, a main shaft and a seed-filling adjusting plate, the seed-metering device shell and the air chamber shell are fixedly connected through a bolt, and the main shaft is rotationally arranged on the air chamber shell along the axis; a feeding port, a seed discharging port and a seed falling port are respectively formed in the seed metering device shell, a seed metering air chamber is formed in the inner side of the air chamber shell, a seed filling adjusting plate is detachably arranged at the feeding port of an inner cavity of the seed metering device shell, a suction support type seed metering disc is detachably arranged in the inner cavity of the air chamber shell, and the suction support type seed metering disc is detachably arranged in the air chamber shell. The suction and support combined type seed-metering disc is adopted, a disturbance increasing and filling assisting structure is provided, and when precise crop seeding is carried out, high-speed seeding is achieved, the suction leakage rate of seeds is effectively reduced, the seed filling qualification rate of the seeds under the high-speed seeding condition is remarkably improved, and the seed breakage rate is reduced. The suction and support combined type seed-metering disc has the advantages that the suction and support combined type seed-metering disc has the advantages that the suction and support combined type seed-metering disc is adopted, and the disturbance increasing and filling assisting structure is provided;