2612 results about "Weed" patented technology

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 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.

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 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.

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.

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 invention discloses a plant protection unmanned aerial vehicle operation method fusing weed recognition and real-time variable spraying, and the method comprises the following steps: employing a plant protection unmanned aerial vehicle to carry a multispectral sensor, collecting multispectral data in real time, and generating a high-resolution multispectral image; converting the radiation intensity value into an actual reflectivity value by using a spectral reflectivity standard plate; according to the generated multispectral image, an NDVI value is calculated in real time through an airborne calculation module; in combination with flight path data, mapping the NDVI index and the geographic position of the flight operation; the classification standard of the NDVI value is adjusted in combination with a machine learning algorithm, and a spraying operation prescription map is generated; the electronic control spraying device adjusts the spraying flow in real time; and establishing a spraying model, and calculating an adjustment coefficient alpha to obtain the average pesticide application amount Y per mu. According to the plant protection unmanned aerial vehicle operation method, the spraying amount can be adjusted according to field weed distribution and crop growth vigor, the precision of plant protection operation is improved, the pesticide usage amount is reduced, and environmental pollution is reduced.

A method for applying at least one spray product to agricultural land having crop plants and weeds. The crop plants and weeds can be assigned to a cotyledon class of monocotyledons or to a cotyledon class of dicotyledons. The at least one spray product is applied to the acquired field section of the agricultural land by means of at least one spray nozzle unit of an agricultural spraying device depending on the cotyledon class of the crop plants and a first ascertained weed identification number and / or a second ascertained weed identification number.

The invention discloses a method for interplanting konjak under a coffee forest, and belongs to the technical field of konjak planting. The problems of konjak yield reduction and irrigation contradiction caused by water and nutrient competition between coffee trees and konjak root systems are solved. Isolation ditches are dug between rows of coffee trees, a root system isolation plate is inserted to separate two crop root system areas, a black plastic mulching film is laid to inhibit weeds and preserve soil moisture, cross-shaped planting holes are formed to position konjac seeds, a drip irrigation belt is arranged in a coffee area, a micro-spraying belt is arranged in a konjac area, and konjac tubers are dug along the outer side of the isolation plate during harvesting. Physical isolation root competition and layered precise irrigation are achieved, the disease and pest spreading risk is reduced, and the land utilization efficiency and the konjak commodity rate are improved.

The present invention relates to the field of image recognition. Provided are a farmland weed detection method and system, and an electronic device. The detection method comprises: collecting a target image containing weeds in farmland; using YOLOv8 based on a RevColNet backbone network to construct a weed detection model, and performing weed recognition on the basis of the weed detection model; and performing precise removal of the weeds. The defects in farmland weed recognition in the prior art of various information of weeds being inadequately described, high recognition accuracy being difficult to obtain, and there being problems such as relatively high calculation complexity, a large number of model parameters and a relatively large model scale are alleviated. The solution in the present application provides a YOLOv8-based improved model, which can more precisely recognize weeds in farmland, and has lower calculation complexity and higher weed recognition efficiency.

The invention discloses a corn field weed detection method and device based on improved YOLOv11. The method comprises the following steps: acquiring and constructing a weed image data set for model training; replacing a backbone network of the YOLOv11n baseline network with a double-flow visual network, and taking the replaced network as a first optimized network; replacing the original pyramid pooling module with an enhanced receptive field module at the tail end of the backbone network of the first optimized network to obtain a second optimized network; in a neck feature fusion layer of the second optimization network, a self-adaptive context guide fusion module is used for replacing traditional splicing, and a third optimization network is obtained; and taking the third optimization network as a final lightweight high-precision weed detection model DEA-YOLO11, and detecting field weeds based on the model. According to the method, the problem of low detection precision caused by insufficient global context sensing capability, fine-grained feature loss and low multi-scale feature fusion efficiency of a weed detection model in a complex agricultural scene in the prior art is solved.

The utility model provides a grass winding prevention device for a rotary cultivator, belongs to the technical field of agricultural machinery, and aims to solve the problem that when an existing rotary cultivator performs rotary tillage operation on land, weeds or platycodon grandiflorum and the like are easily wound on the surface of a rotating shaft, and normal operation of the rotary cultivator is affected. Comprising a rotary cultivator body. The connecting frame is in bolted connection with the upper part of the rotary cultivator main body; the driving mechanism is in bolted connection with the interior of the rotary cultivator main body; the rotary tillage cutter shaft is fixedly mounted in the driving mechanism, and the rotary tillage cutter shaft is rotationally connected to the interior of the rotary cultivator main body; a plurality of groups of rotary tillage blades are arranged, and the plurality of groups of rotary tillage blades are in bolted connection with the interior of the rotary tillage cutter shaft; the connecting and cleaning assembly is arranged in the rotary tillage cutter shaft; and the cutter shaft cleaning assembly is arranged in the rotary tillage cutter shaft. The device has the advantages of being convenient to maintain, capable of avoiding grass winding, convenient to use and the like.

The invention provides a field weed intelligent processing method based on an unmanned aerial vehicle, which is based on an unmanned aerial vehicle image gridding weed density estimation and quantitative spraying control system and is characterized in that the unmanned aerial vehicle image resolution is improved through a super-resolution enhancement technology (ESRGAN), so that small-scale weed features are clearer, and the weed density is improved. Therefore, the weed identification accuracy of the model is improved. Meanwhile, statistics of the number of weeds in different grids is achieved through a density estimation algorithm, the spraying amount is dynamically adjusted according to an estimation result, and quantitative accurate spraying is achieved. According to the method, the weed monitoring precision is improved, the pesticide consumption is greatly reduced, and the environmental protection benefit and the cost benefit of the system are improved.

The invention provides pulling-out type farmland weeding equipment, and relates to the technical field of weeding equipment.The pulling-out type farmland weeding equipment comprises a movable frame, an adjusting component is arranged in the movable frame, a weeding mechanism is arranged below the adjusting component, the weeding mechanism comprises an outer shell, a lifting component is arranged in the outer shell, and an inner shell is rotationally connected to the bottom end of the outer shell; a motor drives a lead screw to rotate, so that an inner disc and an outer disc vertically move downwards in the direction of the lead screw, when a vertical plate moves downwards, the bottom face of the vertical plate and an outer inclined block correspondingly extrude, the bottom ends of the buckets rotate and converge towards the middle, magnetic protruding blocks pop out under the action of springs A to be clamped into T-shaped grooves, and after the bottom ends of the four buckets converge towards the middle, the magnetic protruding blocks are clamped into the T-shaped grooves. The arc-shaped groove is matched with the spring rod to be in contact and abut against the spring rod to drive the whole to rotate, so that corresponding root soil below grass is completely shoveled up, then the motor rotates reversely to complete lifting and pulling-out work, and finally the purposes of thoroughly cutting off and turning out the root of the weed and avoiding regeneration caused by breakage of the root during straight lifting are achieved.

The invention relates to the technical field of agricultural agents, in particular to application of trans-2-hexenal as a weed germination inhibitor. It is found for the first time that trans-2-hexenal has an inhibition effect on wheat field gramineous weed germination under a certain concentration, and then application of trans-2-hexenal as a weed germination inhibitor is provided. The invention provides a better application mode of the trans-2-hexenal in the soil, and defines a wheat sowing safety interval after the trans-2-hexenal is applied. Therefore, the trans-2-hexenal can be used for reducing the grassy weed seed bank in the wheat field soil, the occurrence of wheat field weeds is controlled from the source, the green prevention and control of wheat field weeds are realized, and the use of chemical herbicides is reduced.

The invention discloses a corn field weed detection method and device based on improved YOLOv11, and the method comprises the steps: collecting a weed picture, marking with a LabelImg marking tool, and constructing a data set with various types of weeds; based on the weed data set, performing structure optimization on the YOLOv11 target detection network to obtain a first optimized network; integrating a parameter-free space attention mechanism SimAM module in the first optimization network to obtain a second optimization network; on the basis of the second optimization network, designing an ACC3k2 module, obtaining a third optimization network, and constructing a feature enhancement network; on the basis of the third optimized network, introducing a DySample up-sampling operator to obtain a fourth optimized network; and taking the fourth optimized network as a final lightweight weed detection network, and detecting weeds in the corn field based on the lightweight weed detection network. The device comprises a processor and a memory. According to the invention, a feasible path is provided for realizing agricultural field intelligent weeding, and a technical support is provided for promoting automatic, efficient and green development of agricultural production.

The invention discloses an intelligent weeding control method of a laser weeding robot and the laser weeding robot. The method comprises the steps that ground images are collected through a binocular depth camera, and weeds and seedlings are recognized; judging the relationship between the actual density of the inter-plant weeds and a preset density threshold value; when the actual density is greater than a preset threshold value, setting a first target speed to run the laser weeding robot, and planning a strike path based on the weed position to execute point descending type strike; and when the actual density is smaller than or equal to the preset threshold value, setting a second target speed for operation and starting a surface scanning mode to cover the high-density weed area. Dynamic matching of speed grading and strike strategies is achieved through a density threshold decision mechanism, the weeding rate is guaranteed, meanwhile, the operation efficiency is optimized, the problems of low-density working condition energy waste and high-density working condition clearing efficiency reduction are solved, and the adaptability of a farmland with complex weed distribution is comprehensively improved.

A method for spraying an agricultural field using a spray system that includes a spray boom attached to an agricultural vehicle and including cameras and broadcast nozzles, the agricultural vehicle having a tank that stores a residual herbicide. The method includes capturing images of regions of the agricultural field, each camera associated with one or more broadcast nozzles; automatically analyzing, with a trained machine learning (ML) model, each image for target weed(s); automatically detecting the target weed(s) in one or more images; for each image in which the target weed(s) is detected, automatically spraying first region(s) with one or more first broadcast nozzles at an increased application rate compared to a default application rate; and for at least some of the images in which the at least one weed is not detected, automatically spraying second region(s) with the one or more second broadcast nozzles at the default application rate.

The invention relates to a microbial agent for aerobic composting. The microbial agent comprises bacillus subtilis J2, pseudoarthrobacter psychrotolans J4 and cellulosimicrobium cellulans J6, and the microbial agent is prepared from the following raw materials: bacillus subtilis J2, pseudoarthrobacter psychrotolans J4 and cellulosimicrobium cellulans J6. The microbial agent for aerobic composting is prepared from the following raw materials: bacillus subtilis J2, pseudoarthrobacter psychrotolans J4 and cellulosimicrobium cellulans J6, and the microbial agent for aerobic composting is prepared from the following raw materials: bacillus subtilis J2, pseudoarthrobacter psychrotolans J6. The invention also relates to a preparation method and application of the microbial agent. The microbial agent can quickly increase the composting temperature, effectively kill pathogenic bacteria, worm eggs and weed seeds, shorten the composting time and improve the yield and quality of organic fertilizer.

The invention discloses a farmland weed and external invasion species detection method and device based on an unmanned aerial vehicle and deep learning, and the method comprises the steps: collecting and marking images containing weeds and external invasion species in a farmland, and forming a data set; the method comprises the following steps: constructing a weed and external invasion species target detection framework AriNanoDet model based on NanoDet, adopting ShuffleNetV2 as a backbone network, utilizing ShuffleNetV2Block in a second stage to a fourth stage of feature extraction, and then weighting channel information of an output feature map by applying an SE module; in the multi-scale feature fusion part, a self-adaptive feature selection mechanism is introduced on the basis of a feature pyramid network, a fused feature map is processed through a detection head, a detection result is optimized, and a model is formed and trained; the trained model is installed on an unmanned aerial vehicle for real-time detection, and positioning is carried out according to the detected coordinates of weeds and external invasion species. According to the invention, automatic monitoring of farmland weeds and external invasion species is realized, the labor cost is saved, the detection precision is high, and the detection process is simple and convenient.

The present disclosure relates to herbicidal combinations, compositions and methods for controlling weeds comprising the carbamate herbicide asulam and an additional herbicide selected from the group comprising cyclohexanedione oxime herbicides, pyrimidinylsulfonyhirea herbicides, triazolopyrimidine herbicides, imidazolinone herbicides, alkylazine herbicides, triketone herbicides, N-phenylimide herbicides or chloroacetamide herbicides. Specifically, the additional herbicide may be one of clethodim, chlorimuron, diclosulam, imazamox, imazethapyr, indaziflam, mesotrione, saflufenacil, tiafenacil or S-metolachlor.

The invention belongs to the technical field of low-altitude unmanned aerial vehicle remote sensing and precision agriculture, and discloses a crop canopy coverage high-throughput acquisition method and system based on unmanned aerial vehicle remote sensing. The method comprises the following steps: quantitatively describing a visual feature mode between a crop canopy and a soil background from a high-resolution RGB image; automatically mining association mapping between category semantics and visual feature modes by using a support vector machine method to obtain a preliminary extraction result of crop canopies and soil backgrounds, and generating a single-temporal crop canopy mask map; the method comprises the following steps of: optimizing all single-time-phase crop canopy mask patterns of crop growth by using a dynamic weed elimination method of an RGB (Red, Green, Blue) image time sequence, and respectively counting the number of peanut canopy pixel clusters and the number of soil pixel clusters in each plot cell in each plot to finish accurate estimation of the crop canopy coverage. The invention provides a basic technical means for the fields of agricultural precise management, crop high-throughput phenotype monitoring, digital breeding and the like.

The invention belongs to the technical field of herbicide resistance genes, and relates to a rice herbicide resistance gene and application thereof. The herbicide-resistant gene provided by the invention is OsCYP704A5, and the sequence of the herbicide-resistant gene is as shown in SEQ ID NO. 1. According to the application disclosed by the invention, molecular docking analysis is carried out on the OsCYP704A5 gene and the triazole sulcotrione, and the result shows that the OsCYP704A5 and the triazole sulcotrione have relatively high combining capacity and have a certain promoting effect on detoxification metabolism. The OsCYP704A5 gene is further over-expressed in Nipponbare rice, so that the transgenic rice can obtain the drug resistance to triazole sulcotrione. The HPPD inhibitor herbicide-resistant rice strain can be used for cultivating HPPD inhibitor herbicide-resistant rice varieties, and provides a new possibility for researching HPPD inhibitor herbicide resistance of paddy field weeds (barnyard grass).

The invention discloses an ecological planting method for remarkably improving the amino acid content of citrus, which comprises the following steps: (1) mixing and fermenting pruned citrus branches, weed straws, livestock and poultry manure and indigenous probiotic ferment powder to prepare a basic microbial fertilizer, and adding a calcium magnesium phosphate fertilizer and monopotassium phosphate to prepare a special microbial fertilizer for citrus, applying as a base fertilizer in winter; (2) after citrus pruning, in key phenological periods such as a flower bud period, a young fruit period and a fruit swelling period, an indigenous probiotic enzyme diluent with a specific dilution ratio is used for systematic foliage spraying on a tree body. The indigenous probiotic enzyme contains bacillus amyloliquefaciens, saccharomycetes, active enzymes and a plurality of nutrient elements. The method can significantly increase the total amino acid content of citrus fruits, greatly increase the yield, improve the taste of the fruits, enhance the tree body resistance, reduce the use of chemical pesticides, improve soil, and realize quality improvement, yield increase and green sustainable development of citrus planting.