3967 results about "Weed" patented technology

The invention discloses a weeding path planning method for an agricultural weeding robot. The weeding path planning method comprises the following steps that 1, the weeding robot is started, and whether all sensors and cameras work normally or not is checked; 2, establishing a basic three-dimensional map of the farmland; 3, generating a real-time weed distribution map; 4, identifying the types and forms of the weeds, and marking on the farmland map; 5, generating an optimal weeding path; 6, in the advancing process of the robot, the path is dynamically adjusted to avoid crops; 7, according to the weed density and type, the robot executes a corresponding weeding strategy; 8, optimizing a weeding strategy and a path planning algorithm; and 9, after the weeding task is completed, the robot returns to the initial position or the charging station along the shortest path to prepare for next operation. The weeding efficiency and accuracy are improved, unnecessary repeated weeding and herbicide waste are reduced, and the weeding effect of the whole farmland is optimized.

The invention discloses a weed recognition method and system based on a convolutional neural network and application in pesticide spraying weeding, the weed recognition method based on the convolutional neural network comprises a weed recognition model based on YOLOv8-seg, and the weed recognition model has the characteristics of model trunk lightweight, detection precision and the like. According to the method, firstly, a new lightweight backbone network is constructed by using an improved ShuffleNetV2 and StarNet network, secondly, a C2fStar module is innovatively proposed, the model precision is improved while the parameter quantity is reduced, and an ECA attention mechanism module is added to a model network node; the precise pesticide spraying method and system related to weeding application comprise an algorithm for weed positioning, grading and path planning and a precise variable pesticide spraying algorithm, and the precise variable pesticide spraying method and system are adopted, so that the use of herbicides and the pollution to the environment can be effectively reduced while precise and efficient weeding is achieved.

The invention relates to the technical field of agricultural machinery, in particular to an orchard intelligent all-dimensional mechanical weeding machine which comprises a frame, a controller is fixedly connected to the top of the frame, a camera is fixedly connected to one side of the frame, the controller is used for receiving image information collected by the camera, and an energy supply assembly for supplying energy to the mechanical weeding machine is arranged at the top of the frame. A driving assembly used for driving the mechanical weeding machine to move and a reversing assembly used for controlling the mechanical weeding machine to reverse are arranged at the bottom of the frame, and a monitoring assembly used for monitoring the current environment condition and an adjusting assembly used for adjusting the weeding height are arranged on one side of the frame; and a control system for controlling the mechanical weeding machine to operate is arranged in the controller. Orchard images are collected in real time through the camera and the recognition module, crops and weeds are accurately distinguished on the basis of the database established through data over the years, the weeding accuracy is improved, and meanwhile the possibility that the crops are accidentally damaged by the mechanical weeding machine is greatly reduced.

The invention relates to the technical field of computer vision, and particularly discloses a weed root recognition method and device based on attitude estimation, and the method comprises the steps: obtaining field image data containing a weed plant, detecting a leaf key point through a pre-trained leaf attitude estimation model, fitting a leaf vein extension line, calculating an intersection point cluster, and carrying out the recognition of a weed root. And screening a high-density candidate point set through density clustering analysis, calculating a weighted center coordinate as a weed root positioning result according to density weight, and transmitting the weighted center coordinate to laser weeding equipment to execute precise weeding operation. According to the method, accurate weed root identification is realized, the positioning accuracy is higher, the robustness to a complex field environment is higher, the real-time performance can be improved by utilizing model lightweight, efficient deployment is realized, and the data labeling cost is reduced.

The invention discloses a multi-input model compensation plant protection robot pesticide application control system and control method, and belongs to the technical field of agricultural plant protection. The system comprises an upper computer input system, a lower computer controller and a pesticide application system; the upper computer input system is based on an image processing unit of an AI edge computing platform, performs target identification by using a deep learning model, and extracts key information of target crops or weeds in real time; the lower computer controller receives target information transmitted by the upper computer, adopts a dynamic front adjustment strategy of a spray boom virtual line, and dynamically controls an execution element according to the real-time traveling speed of a vehicle, sensor data of a pesticide application system and key information of target crops or weeds, so that accurate target pesticide spraying is realized; the pesticide applying system comprises a pesticide box, a plunger pump, a flow sensor, a pressure sensor, a switch valve, an electric ball valve, an overflow valve, a PWM proportional electromagnetic valve and a nozzle array. The pesticide applying precision is improved, the pesticide using amount is reduced, and the sustainability of agricultural production is improved.

Disclosed herein are methods, devices, modules, and systems which may be employed for accurate targeting of objects. A method of point-to-point targeting may be implemented by systems with two or more sensors to locate an object and coordinate a handoff between the two or more sensors. These methods, devices, modules, and systems may be used for automated crop cultivation or maintenance. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.

The invention relates to the technical field of agricultural mechanical equipment, and provides a laser weeding method of a weeding robot and the laser weeding robot, the weeding method comprises the following steps: obtaining weed information, and selecting a weeding mode based on the weed information; laser parameters are adjusted based on the selected weeding mode, and path planning is carried out; controlling the weeding robot to advance according to the planned path, controlling the laser beam to align with weeds to perform weeding operation, and continuously feeding back the real-time position of the laser beam; and comparing the real-time position with a predetermined target position, and maintaining the current position or adjusting the current position based on a comparison result. According to the method, the laser parameters can be adjusted based on the weeding mode, the path can be planned, and the mode can be suitable for various complex weeding environments and can adapt to the weeding environments better, so that weeds are accurately and rapidly removed.

The invention discloses an intelligent agricultural weeding track control system, which relates to the field of agricultural related technologies and comprises an identification and positioning module, a track planning module, a navigation optimization module, a collaborative operation module and a man-machine interaction module. The invention further discloses an intelligent agricultural weeding track control method which comprises the steps that the unmanned aerial vehicle or the ground robot cruises above a farmland or on the ground, the depth camera collects farmland image data in real time, the image processing unit conducts denoising, filtering and edge detection processing on the original image data collected by the depth camera, and the weeding track is obtained. The two-dimensional image and the three-dimensional point cloud data are registered, so that the two-dimensional image and the three-dimensional point cloud data are consistent in space, and weeds and crops are distinguished through an image recognition technology. Through the path planning algorithm, the optimal weeding path can be calculated according to the actual terrain, weed distribution and obstacle conditions of the farmland, blind walking and repeated operation of the robot in the operation process are avoided, and therefore the weeding efficiency is greatly improved.

The utility model discloses intelligent weeding equipment based on visual navigation, and relates to the technical field of intelligent weeding equipment. In order to solve the problems that transverse movement in a field and increase and decrease of the operation number of weeding components of an existing weeding machine in the using process need manual adjustment, ridges are prone to being damaged in the working process, the using convenience of the weeding machine is reduced, mechanical weeding mostly refers to inter-row weeding, the inter-plant weed effect is not ideal, weeds cannot be intelligently recognized, and the weeding efficiency is high. And the operation path of the weeding machine is planned for efficient weeding. The device comprises a connecting frame, a translation mechanism, a turnover mechanism and a visual navigation mechanism, one side of the connecting frame is movably connected with a weeding mechanism, the other side of the connecting frame is connected with a supporting wheel, and the weeding mechanism comprises a rack, a weeding shovel and weeding fingers; the rack is movably connected to the connecting frame, a plurality of weeding shovels and a plurality of weeding fingers are arranged on the rear side of the rack, and a visual navigation mechanism is arranged at the upper end of the rack. The farmland weeding machine is used for farmland weeding.

A robotic module of a weed removal system is disclosed, with a mounting interface to support the robotic module on a frame. An end effector is displaceable between an elevated and a down position, with an actuation unit engaged to the end effector to cause such displacement. A detection unit is included, for capturing images of a ground surface and monitoring a position of the system relative to a detection area moving relative to the system. A controller includes a processing unit, and a non-transitory computer- readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable for receiving the images and position data from the detection unit, processing the images to detect the presence of weeds in the detection area, associating position data received from the detection unit to weeds in the detection area, and driving a displacement of the end effector to reach a weed.

The invention discloses a method for improving pasture productivity by optimizing a mixed sowing proportion and a sowing mode, which comprises the following steps of: performing rotary tillage on soil and applying humus before freezing in winter, turning over the soil and applying a base fertilizer after reviving in spring, then selecting leguminous plants and gramineous plants according to a weight ratio of 3: 7, respectively and uniformly mixing seeds of two functional groups, the method comprises the following steps: ditching, ridging and sowing gramineous plant seeds in line, sowing leguminous plant seeds in holes between two rows of gramineous plants 20-30 days after the gramineous plant seeds emerge, carrying out topdressing and pest and weed damage management after sowing, mowing the overground part of the grassland at the last stage of plant growth, and controlling the stubble height of the leguminous plants and the gramineous plants. By accurately optimizing the mixed sowing proportion and sowing strategy of leguminous and gramineous pasture and combining the complementarity of plant functional groups in ecological niche (time and space), an innovative pasture mixed sowing method is provided, and theoretical support and technical guarantee are provided for efficient and sustainable development of animal husbandry production.

The present invention relates to a method for post-emergent weed control, which comprises applying an effective amount of a composition comprising herbicide A (component A) and saflufenacil (component B) to emerged weeds, or an area, where weeds are growing.

The invention relates to a tracking navigation method for positioning a crop center point based on laser radar and image fusion, and belongs to the technical field of navigation, and the method comprises the steps: carrying out the marking of a farmland image after distortion correction, generating a training sample, inputting the training sample into a U-net model, carrying out the training, obtaining a crop segmentation model, and carrying out the classification of crops and weeds; performing feature fusion on the filtered laser radar point cloud data and the farmland image after distortion correction to obtain crop point cloud with semantic information; calculating a crop center point according to the crop point cloud with the semantic information; and providing navigation for the agricultural robot by taking the crop center point as a preview point. The spatial structure information of the target crop is obtained through the laser radar and the camera, the semantic point cloud is generated through feature fusion, the crop center point is dynamically extracted from the point cloud to serve as a real-time preview target, a fixed preview distance mechanism in a tracking algorithm is replaced, centimeter-level precise navigation is achieved, and the precision of the target crop is improved. And the operation precision and the operation safety of the agricultural robot are greatly improved.

A change of state of weed seeds to having reduced germination viability in under one minute by illuminating a seed with at least one of 2 J / cm2 cumulative illumination energy, and 0.2 W / cm2 irradiance, but no more than 7 W / cm2 average irradiance, of at least one of an Indigo Region Illumination Distribution (IRID), and infrared radiation that is substantially Medium Wavelength Infrared (MWIR) radiation, preferably 2-8 microns. The MWIR radiation from heated borosilicate glass or glass powder at just under 500 C offered a peak MWIR emission of 3.75 microns was unexpectedly effective, and can be used in a radiant and transmissive weed seed accumulator transport belt. The process can be incorporated into a harvester combine to convert a tailings flow prior to discharge on an agricultural field. An illuminated harvest combine using an illuminator according to the invention allows reduction of the weed seed bank.

Burkholderia, and a composition, use and usage method thereof. Provided are the Burkholderia, and a composition comprising the strain and / or a metabolite, culture, fermentation broth and / or extract thereof. The Burkholderia or the composition thereof can generate a siderophore, induce plants or seeds thereof to generate systemic resistance to plant pathogens, and reduce the chemotaxis of plant pathogens to plants, so that prevention and treatment of plant diseases and insect pests are achieved. In addition, plant root system development can be promoted, a nitrogen fixation effect is provided, a phosphate solubilization effect is provided, indoleacetic acid is generated, and weeds are removed, thereby promoting plant growth.

The invention relates to the technical field of agricultural automation and intelligent control, in particular to a weeding monitoring system and method based on an unmanned aerial vehicle, and the method comprises the steps: collecting spectral reflection data and microwave internal structure data of a target area through a multispectral camera and a microwave sensor carried by the unmanned aerial vehicle; the system generates standardized perception data through a data fusion module, and performs precise classification and recognition of weeds in combination with feature extraction, adaptive fuzzy clustering and multi-model collaborative learning technologies; based on a spatial distribution diagram of weeds, the system predicts a weed diffusion trend through a graph convolutional network, and an optimal weeding strategy is generated in combination with environmental data; and the unmanned aerial vehicle executes the weeding task through the optimized flight path, monitors the weeding effect in real time, continuously optimizes the strategy, and ensures efficient execution of the weeding task.

The invention provides an annual intercropping green manure planting method suitable for orchards in plateau areas. According to the method, tartary buckwheat and vicia villosa are mainly adopted as green manure plants, and a plurality of key steps of land parcel selection, soil preparation before sowing, variety selection, seed treatment, green manure sowing, plant management, cutting and covering and the like are clearly stipulated. The method can improve the utilization rate of nutrients in the soil, increase the content of organic matters in the soil, improve the soil quality, effectively inhibit growth of weeds between rows and on ridges of the organic apple orchard, reduce the weeding cost, and solve the problem of high labor cost caused by long-term ploughing and weeding of the organic apple orchard in the plateau mountain area of Yunnan province. Excessive dependence on organic fertilizer is effectively relieved, and the method is an annual intercropping mode for comprehensively improving ecological benefits and economic benefits of the orchard.

Compounds having herbicidal activity effective against economically important harmful plants are disclosed. The disclosed compounds exhibit herbicidal activity even at relatively low application rates, while at the same time may exhibit compatibility with crop plants. The disclosed compounds may further be effective and efficient against a large number of weeds and against a broad spectrum of weed grasses. As disclosed herein, 3-phenylisoxazoline-5-carboxamides of S-containing cyclopentenylcarboxylic esters of formula (I) and their salts exhibit herbicidal activity against a broad spectrum of economically important mono- and dicotyledonous annual harmful plants.

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

The system can include a detection model; an optional camera system; and an optional control system. The system can function to detect plants within a field. Additionally, the system can function to facilitate an agriculture operation(s) based on the positions of plants within the field. Variants of the system can be configured to (autonomously) perform and / or facilitate agriculture operations which can include: agent dispersal (e.g., solid agent dispersal), fluid spraying, crop imaging (e.g., crop data collection), side dressing, weeding (e.g., mechanical actuation, targeted laser ablation, etc.), harvesting, planting, tilling, fertilizing, irrigating, and / or any other suitable operation(s). Variants of the system and / or method can be used to facilitate detection and / or agriculture operations for single crops, multi-crops (e.g., crop doubles, where agriculture operations may be based on stem proximity), ground cover plants, weeds, and / or agriculture operations in any other suitable scenarios.

The invention belongs to the technical field of agricultural robots, and discloses a visual perception digital twinborn cotton weeding robot identification obstacle avoidance method, which comprises the following specific steps: step 1, establishing a digital cotton and weed database, cotton field image information and cotton field environment information of real scenes in the same area under the condition of changing illumination in different time periods of the whole day are collected through a data collection system, and timestamps during collection and collected real-time data are transmitted to a cloud server. Through mutual cooperation of the data acquisition system, the cloud server and the virtual simulation platform, a cotton and weed virtual scene is established, through simulation of a cotton field and a weeding robot on the simulation platform, time-varying digital scene construction is perfected, and the time-varying digital scene construction efficiency is improved. The problem that an existing digital scene is not perfect in reduction of the time-varying process of the influence of illumination on the cotton field in the real environment is solved, and therefore the consistency of the virtual scene and the real environment and the weeding accuracy are further improved.

A method for selectively controlling weeds in monocotyledonous crops, which comprises applying to the crop cultiva- tion area an effective amount of a herbicide A selected from a compound of formula (I) and salts of the compound of formula (I); wherein R is hydrogen, (C1-C6)-alkyl or (C3-C6)-cycloalkyl.

The invention relates to an intelligent laser weeding method and device. The method comprises the steps that S1, a navigation path is formulated for a weeding device; s2, issuing the navigation path; s3, after weeding work is started, weed images are shot in the field walking process of the weeding device; identifying and judging the types of weeds and the sizes of plants in the identified images; s4, determining the laser irradiation area of each weed region, and correcting the laser irradiation power of each weed region in real time according to the type of weeds and the plant size; s5, determining position adjusting parameters of the laser and the size of an irradiation light spot; and S6, moving the laser to a position right above each weed area, controlling the laser to emit laser downwards, adjusting a laser spot to a corresponding size, and controlling the laser power to a corresponding correction value to irradiate the weed area. By means of the method, the laser irradiation power can be automatically adjusted in real time according to the types of weeds and the sizes of plants, the weeding effect can be improved, and meanwhile unnecessary energy waste is reduced.

Weed seeds are destroyed in the chaff from a combine harvester by primary and secondary destructors where the primary feeds to the secondary. The secondary can use repeated high speed impacts caused by a rotor mounted in one of a pair of side by side housings which accelerate the discarded seeds in a direction centrifugally away from the rotor onto a stator including angularly adjustable stator surfaces around the axis. The primary can also use an impact arrangement of a different and simplified structure or can use another destruction modality such as radiation. The primary is mounted at a transfer device including a transverse auger which carries the chaff and weed seeds from the rear of the sieve to the secondary.

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

The present disclosure relates to, inter alia, a weed control method. In one example, a method comprises applying to the locus a weed controlling amount of a pesticide composition comprising a FatA acyl-ACP thioesterase-inhibiting herbicide, wherein the crop plants are modified such that they comprise a FatA acyl-ACP thioesterase which provides the crop plant with tolerance against the FatA acyl-ACP thioesterase-inhibiting herbicide. Recombinant polynucleotides and suitable for use in the methods, and edited FatA acyl-ACP thioesterases are also disclosed.

The invention discloses a fertilization and topdressing intelligent decision-making system and method based on rice open space sensing data. The method comprises the following steps: acquiring agricultural condition data through a multi-source data acquisition module formed by an unmanned aerial vehicle multispectral camera, a ground sensor network and a meteorological station; generating a standardized data set by adopting a space-time fusion algorithm; the CNN-LSTM hybrid model is used for realizing rice growth prediction, disease, pest and weed identification and maturity evaluation; dynamically generating a base fertilizer / topdressing prescription map, a pesticide application scheme and an agricultural machine path plan in combination with an agricultural rule base; and the intelligent agricultural machine is linked through the Internet of Things interface to execute accurate operation. Millimeter-level sensing of the farmland state, variable control over fertilizer application and pesticide application and autonomous execution of agricultural machine operation are achieved, and compared with a traditional mode, the fertilizer utilization rate is increased by 35% or above, the pesticide application amount is reduced by 40%, and the comprehensive yield is increased by 15-20%.

The invention relates to the technical field of agricultural intelligent equipment, in particular to a field sowing grain distance detection system and method based on computer vision, and the method comprises the steps that an image collection module collects 4KRGB and near-infrared data of a sowing area; the image processing module is used for processing and correcting the acquired image in real time; the grain distance feature deep learning analyzer strengthens seed feature extraction through an attention mechanism based on an improved YOLOv5 algorithm, accurately distinguishes weeds from seeds, and calculates a center distance deviation value of adjacent seeds based on a preset target grain distance; the positioning fusion module fuses Beidou positioning data and a visual odometer track, generates seed longitude and latitude and burial depth coordinates by using a high-precision farmland surface elevation model, and maps the seed longitude and latitude and burial depth coordinates to a farmland electronic map; and the dynamic regulation and control execution mechanism adjusts the rotating speed of the seeder or the seeding gap according to the grain distance deviation value and a priority strategy, and dynamically corrects the grain distance. Therefore, the problems of high detection error rate, single detection algorithm, poor data relevance and the like in the prior art are solved.

The utility model relates to a ploughing machine for wasabi planting, and belongs to the technical field of wasabi planting. The hydraulic rod drives the ploughing assembly to rotate, the angle of the ploughing ploughshare can be flexibly adjusted according to the ploughing depth requirement, operation is easy and convenient, in the walking process of the device, soil is ploughed through the ploughing ploughshare, meanwhile, the wheel shaft rotates and drives the first crushing roller and the second crushing roller to rotate, the ploughed soil blocks are crushed, and the ploughing efficiency is improved. Soil blocks which are not completely broken are harrowed again through the harrowing assembly, so that the ploughing effect of the soil is guaranteed, large soil blocks are avoided, the air permeability and water retention of the soil are further improved, growth and development of roots of wasabi are facilitated, secondary rework is not needed, and the ploughing efficiency is improved; the harrowing assembly can harrow crop stubbles and weeds in a planting field and is matched with the second crushing roller to turn the crop stubbles and the weeds into soil, and therefore the planting quality of wasabi is improved.