247 results about "Row crop" patented technology

The invention relates to the technical field of agricultural irrigation, in particular to an irrigation decision-making method based on an agricultural system model. The method comprises the following steps: calculating a growth environment index of a target farmland to generate a crop environment growth index; crop physiological status evaluation is carried out according to the crop environment growth index, short-term water demand prediction of the field is carried out, and field crop short-term water demand data is generated; performing irrigation scheme decision processing according to the short-term water demand data of the field crops to obtain optimal irrigation scheme data; irrigation equipment is regulated and controlled by using the optimal irrigation scheme data, and irrigation water quantity influence analysis is carried out to generate irrigation water quantity influence data; and performing adaptive irrigation control optimization according to the irrigation water quantity influence data so as to obtain intelligent irrigation control strategy data. Irrigation equipment can be regulated and controlled according to dynamic requirements of crops, intelligent and accurate irrigation decision control is achieved, excessive or insufficient irrigation is effectively avoided, and the water resource utilization rate is remarkably increased.

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention relates to the technical field of intelligent decision making, in particular to an accurate fertilization intelligent decision making method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a multi-source remote sensing image which comprises an unmanned aerial vehicle image and a stationary satellite image; acquiring soil nutrient data and fertility indexes of the sample field parcels; acquiring meteorological data and soil data; carrying out image preprocessing and feature extraction; constructing a crop identification and classification model; carrying out crop type identification and block division on the multi-source remote sensing image; extracting crop growth features based on the multi-source remote sensing image and the result of the crop identification and classification model; based on the crop growth characteristics and the soil nutrient data, analyzing the relationship between the crop growth stage and the nutrient demand; constructing a nutrient utilization efficiency maximization model; based on a nutrient utilization efficiency maximization model, generating a precise fertilization decision scheme; and outputting a precise fertilization decision scheme, and carrying out precise analysis and intelligent decision making on the growth state and nutrient demand of farmland crops.

The invention provides an intelligent agriculture method and system based on the Internet of Things and artificial intelligence, and the method comprises the steps: collecting and selecting environment parameters and crop growth state data in a target greenhouse in real time, obtaining high-precision environment parameters and crop growth state data, carrying out the scale conversion and integration of the high-precision data, and obtaining a high-precision environment parameter and crop growth state data; the method comprises the steps of obtaining first structured data, structuring historical experimental data into a sensor-like data format to obtain second structured data, analyzing and outputting the confidence coefficient of the first structured data, and performing crop growth prediction on the first structured data. And obtaining a crop growth state prediction value, a crop yield prediction result and a crop growth quality grade, and generating an optimal greenhouse environment regulation and control strategy and a farming management decision suggestion through an intelligent optimization algorithm. Based on an Internet of Things system and artificial intelligence, the accuracy of collected crop related data is ensured, and intelligent greenhouse environment regulation and control are carried out.

The invention discloses an AI-based digital twinborn irrigation area automatic irrigation method and system, and relates to the technical field of artificial intelligence, and the method comprises the following steps: collecting data, and constructing a digital twinborn model; irrigation demand prediction is carried out according to the digital twinborn model; making an irrigation control decision according to the irrigation demand prediction result; performing automatic irrigation based on the irrigation control decision content; crop growth evaluation is carried out according to the irrigation execution result; performing irrigation strategy optimization according to a growth evaluation result; maintaining and updating the irrigation system according to the irrigation strategy; and performing water resource management based on the planning result. According to the method, the digital twin model can be updated in real time and adapt to environmental changes by introducing the data stream based on the Internet of Things and a reinforcement learning mechanism. The feedback mechanism based on AI and reinforcement learning enables the digital twinborn model to more accurately simulate an irrigation environment and provide an optimal irrigation strategy under different environmental conditions.

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

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 invention relates to a field water and fertilizer intelligent control method based on the Internet of Things, and the method comprises the steps: carrying out the crop type recognition and growth stage recognition through collecting crop images, soil water and fertilizer data and meteorological prediction data, and generating an initial water and fertilizer control scheme of a current time node; a soil water and fertilizer prediction model is created to predict the soil water and fertilizer change trend in a specified time period, the initial water and fertilizer control scheme of the current time node is adjusted according to the predicted soil water and fertilizer change trend, growth cycle characteristics corresponding to crop types are obtained based on big data, and a dynamic water and fertilizer control scheme is formulated. A water and fertilizer adjusting control scheme is executed at a current time node, a dynamic water and fertilizer control scheme is executed at a specified time node, the field water and fertilizer intelligent control method integrating Internet of Things sensing, intelligent identification, trend prediction, dynamic planning and closed-loop feedback is realized, the whole process intelligence from data acquisition to scheme execution is realized, and the water and fertilizer utilization efficiency is improved. The agricultural production cost is reduced.

The invention discloses a crop leaf disease diagnosis method and system driven by a large language model. Comprising the following steps: firstly, extracting a leaf disease image of a target crop and corresponding text semantic information from a basic crop management file so as to obtain an initial crop disease knowledge database; then, the initial crop disease knowledge database is optimized and updated in combination with visual understanding of a large language model; and then, constructing a crop disease classification agent model, inputting a to-be-diagnosed leaf disease image into the crop disease classification agent model, performing crop disease diagnosis by the crop disease classification agent model according to the crop disease knowledge database, and outputting a disease identification result. Under the condition that large-scale image training is not carried out, disease diagnosis can be accurately carried out on the crop disease images, disease image diagnosis requirements of different crops can be met, and the problem that existing work is insufficient in potential in the aspects of zero sample application and expandability is solved.

The invention discloses a vision-based agricultural unmanned aerial vehicle terrain following method and device. The method comprises the steps of S1, performing off-line adversarial training on a GAN network; s2, fusing infrared and visible light images obtained in real time by using a trained GAN network generator to enhance terrain information; s3, estimating the crop canopy distance by using the depth estimation model; s4, taking the obtained crop canopy depth map Dp as a constraint of an objective function, and comprehensively considering other constraint conditions to construct the objective function so as to optimize trajectory planning; s5, optimizing the objective function by adopting a genetic algorithm, and taking an optimized curve as a flight path of the unmanned aerial vehicle to realize terrain following flight; the system can be applied to crop monitoring, pesticide spraying and the like, is particularly suitable for agricultural operation in complex terrains such as hills and mountainous regions, improves the production efficiency and the operation quality, and promotes informatization and intelligentization development of agricultural machinery equipment in China.

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

The invention relates to a resource calling control system and method for a water conservancy irrigation area project, and relates to the field of intelligent control, the request water supply amount of a request node is received, the sum of the request water supply amount is updated, and when the sum is greater than or equal to the redundant water storage amount, the crop type, the crop planting duration, the meteorological information and the soil information of the request node are extracted, and a water supply portrait is configured; retrieving water demand time sequence information of the sample group meeting the water supply portrait, and performing water deficit calculation in combination with the supplied water time sequence information to obtain water deficit time sequence information; performing crop growth data statistics according to the water deficit time sequence information to obtain a crop damage probability; the request nodes are ranked according to the crop damage probability from large to small, the first node ranking result is obtained for water supply resource calling, and the technical problems that the water supply quality is not matched with the demand and the irrigation effect and the agricultural production benefit are affected due to the fact that accurate scheduling for comprehensively considering the actual water use demand of each demand point and the irrigation priority is lacked are solved.

The invention discloses a time sequence SAR image-based plot-level crop classification method and device and a medium, and relates to the technical field of remote sensing. The method comprises the following steps: acquiring land parcel data; constructing a multi-level knowledge reasoning framework, and speculating crop types by utilizing attribute information and external knowledge of the land parcels to obtain reasoning confidence corresponding to the crop types planted in each land parcel; based on vector data of boundaries of the land parcels, time sequence features of each land parcel are extracted from the time sequence SAR images, the time sequence features of each land parcel are associated with crop types, and a standard crop time sequence feature library is established; and comparing the to-be-identified land parcel with the standard crop time sequence feature library, calculating the observation confidence coefficient, fusing the observation confidence coefficient with the reasoning confidence coefficient to obtain a fusion confidence coefficient, and identifying the crop type of the to-be-identified land parcel by using the fusion confidence coefficient. The method provided by the invention solves the problem that the traditional method is low in recognition precision under small samples and complex scenes.

The invention belongs to the technical field of agriculture, and particularly relates to a method for improving thin, thin and hard black land to make nutrients and moisture efficiently utilized, in particular to a method for improving thin, thin and hard black land to make nutrients and moisture efficiently utilized. According to the method for improving the thin, thin and hard black land and improving the efficient utilization of nutrients and moisture of the thin, thin and hard black land, aiming at the characteristics of thin and hard land parcels, wide-narrow-row crop rotation, straw returning to the field and organic fertilizer applying are adopted, straw returning to the field and organic fertilizer applying are adopted to supplement soil organic matter, and a strip ploughing technology is adopted on a machine tool, so that the yield of the soil is increased; soil is not moved in a large area, and a strip tillage machine is only adopted to perform strip tillage operation at the seedling zone to form a seedbed structure with the upper part open and the lower part solid; an intertillage and deep scarification technology is adopted, a deep scarification machine for efficiently utilizing water and fertilizer in a thin and hard black soil area is used, a plow pan is broken, upper water and lower water are communicated, a rat channel component is arranged, a rat channel is formed after deep scarification, a small reservoir and a soil layer are formed below a plough layer, straw is fully returned to the field, organic fertilizer is additionally applied, and soil hardening and water and soil loss are reduced. The organic matter content and activity of the black soil layer are gradually recovered.

A rear module, as illustrated with tanks, seed blowers, and transfer pump for fertilizer is mounted on the existing design frame, axle, and steering system; but it is a separate, distinct module. The planting module and the harvesting module must be disconnected from the common front module power unit and switched for planting row crops or harvesting. The harvesting configuration can have an attachment to the header to seed non row crops, such as, a cover crop like rye or canola Such rear module can be the rear unit of an articulated vehicle, such as a harvester.

A rear module, as illustrated with tanks, seed blowers, and transfer pump for fertilizer is mounted on the existing design frame, axle, and steering system; but it is a separate, distinct module. The planting module and the harvesting module must be disconnected from the common front module power unit and switched for planting row crops or harvesting. The harvesting configuration can have an attachment to the header to seed non row crops, such as, a cover crop like rye or canola Such rear module can be the rear unit of an articulated vehicle, such as a harvester.

The invention relates to the technical field of intelligent agriculture, in particular to an irrigation area measurement and control integrated gate intelligent scheduling system and method, and the system comprises a data collection module, a gate control module, a data processing and analysis module, an intelligent scheduling module and a monitoring and early warning module. By integrating a water level sensor, a flow meter, a weather station data interface, a soil humidity sensor and an agricultural satellite data interface, various agricultural data and hydrological data in an irrigation area are collected in real time, and the crop growth condition, the water demand, the rainfall capacity, the evaporation capacity and the like are analyzed by utilizing historical data and models; therefore, an accurate irrigation scheduling strategy is formulated and executed in the intelligent scheduling module, water supply needed by crop growth is ensured, meanwhile, the system monitors agricultural data and hydrological data in an irrigation area in real time through the monitoring and early warning module, a reasonable threshold range is set, early warning can be triggered in time once an abnormal condition occurs, and the system is safe and reliable. And a reliable decision basis is provided for irrigation area management.

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

The invention provides a crop rotation planting zoning method based on Arc-GIS and meteorological factors. The method comprises the steps of data collection, data processing, spatial analysis, suitability evaluation, visual output and continuous monitoring and updating. Meteorological data comprises a plurality of factors such as air temperature, day and night temperature difference, effective accumulated temperature, active accumulated temperature and precipitation, unary linear regression analysis is carried out through analytical statistics software, and interpolation is carried out through a Kriging interpolation method by utilizing Arc-GIS software, so that a surface element vector data graph of meteorological factors suitable for the growth period of crops is obtained. And crop planting regions are obtained through spatial analysis, and finally, the planting regions are classified according to crop suitability and visual output is carried out. The invention provides a scientific and systematic crop rotation planting zoning method.

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

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

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

The invention provides an open environment crop chlorophyll fluorescence parameter imaging detection method and device, and the method comprises the steps: carrying out the crop canopy segmentation of a crop canopy hyperspectral image based on a crop canopy mask image, and obtaining a crop canopy image; performing hierarchical segmentation based on the crop canopy image according to a clustering algorithm to obtain an upper-layer leaf image and a lower-layer leaf image; determining upper-layer chlorophyll fluorescence intensity of the upper-layer leaf image and lower-layer chlorophyll fluorescence intensity of the lower-layer leaf image; respectively compensating the upper chlorophyll fluorescence intensity and the lower chlorophyll fluorescence intensity based on the balanced brightness and crop absorption photosynthetically active radiation to obtain an upper chlorophyll fluorescence signal correction value and a lower chlorophyll fluorescence signal correction value; and constructing a visual chlorophyll fluorescence parameter spatial distribution image based on the upper-layer chlorophyll fluorescence signal correction value and the lower-layer chlorophyll fluorescence signal correction value. The method can realize rapid and accurate imaging detection of the chlorophyll fluorescence parameters of the crop canopies in the open environment.

The invention provides a regeneration crop rolling-resistant variety identification method based on unmanned aerial vehicle laser radar point cloud, and the method comprises the steps: obtaining three-dimensional point cloud data of a target monitoring region from a crop multi-temporal global image collected by unmanned aerial vehicle laser radar equipment, rolling row crop points and non-rolling row crop points are extracted from the three-dimensional point cloud data; respectively extracting rolling resistance indexes of the crops from the rolling row crop points and the non-rolling row crop points; and predicting the rolling resistance of the crop according to the rolling resistance indexes corresponding to the rolling row crop points and the non-rolling row crop points, and determining whether the crop is a rolling-resistant variety or not according to the rolling resistance of the crop. According to the method, high-throughput identification is automatically performed on the rolling-resistant varieties of the regenerated crops, and the screening efficiency of the high-yield varieties of the regenerated crops can be improved.

The invention belongs to the technical field of unmanned aerial vehicle planning and image processing, and particularly relates to a farmland checking method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium. The method comprises the following steps: designing a flight path covering the whole field based on farmland area and topographic characteristic data; based on the flight path, an unmanned aerial vehicle is adopted for shooting, and farmland images are obtained; preprocessing the farmland image to obtain processed data; inputting the processed data into a constructed recognition model, and performing crop category recognition to obtain a recognition result; and based on the identification result, generating a verification report, and completing verification. According to the invention, the flight path covering the whole field is designed based on the farmland area and topographic characteristic data, and it is ensured that the unmanned aerial vehicle can efficiently and comprehensively acquire farmland images. According to the method, the accuracy of crop category identification is improved, a detailed check report can be quickly generated, and a scientific decision basis is provided for an agricultural management department.

The invention relates to the technical field of greenhouse plant protection machinery, in particular to a facility low-volume spraying device and a spraying method.The facility low-volume spraying device is arranged at one end in a greenhouse in the first direction, and multiple rows of crops are planted in the greenhouse in the first direction; the spraying mechanism comprises a walking mechanism, a lifting mechanism, a canopy detection device, a spraying mechanism body and a controller, the walking mechanism can move in the second direction, the lifting mechanism is arranged on the walking mechanism, and the lifting mechanism is provided with a liftable supporting table; the canopy detection device is arranged on the supporting table, the canopy detection device is used for detecting a point cloud picture of crop canopy distribution in a first plane, the spraying mechanism is arranged on the supporting table, the spraying mechanism comprises a spray head used for spraying pesticide, and the spray head can swing in the first plane; according to the automatic pesticide spraying device, pesticide spraying operation can be completed without entering crop rows, the occupied space is reduced, and the utilization rate of the internal space of the greenhouse is increased.

The invention discloses an agricultural machinery intelligent scheduling method and system based on yield prediction, and belongs to the technical field of intelligent agricultural machinery, and the method comprises the steps of data collection, data preprocessing, crop yield prediction, agricultural machinery intelligent scheduling and comprehensive agricultural machinery scheduling. According to the method, crop yield prediction is carried out by adopting the time convolution feature enhanced gated circulation network, so that the method has better long and short term time sequence dependence modeling capability, key features can be adaptively enhanced, the stability and accuracy of crop yield prediction are improved, and the reliability of subsequent agricultural machinery intelligent scheduling is improved; the discrete particle swarm optimization algorithm is adopted to perform agricultural machinery intelligent scheduling, so that the method has relatively strong global search capability, can adapt to different agricultural production environments and scheduling requirements, has significant advantages in large-scale agricultural production, and improves agricultural machinery scheduling efficiency; comprehensive agricultural machinery intelligent scheduling based on yield prediction is adopted, crop yield difference is fully considered, agricultural machinery operation is reasonably arranged, and resource waste is effectively avoided.

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.

An intelligent weeding automatic row seedling zone center line extraction method relates to the technical field of detection, and comprises the following steps: collecting corn crop images, and carrying out data preprocessing on a crop data set; carrying out algorithm improvement on the YOLOv5s model; the improved YOLOv5s model is trained on the basis of the preprocessed crop data set; utilizing a trained improved YOLOv5s model to identify crops, acquiring coordinate information to describe the position of a bounding box, fitting crop coordinates of a crop row through a least square method, calculating center lines of crop rows on two sides, calculating deviation between weeding machinery and the center lines, and realizing automatic row alignment.

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.