1883 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 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.

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 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 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 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.

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.

The invention discloses a field weed detection method and system based on a lightweight deep learning model, and relates to the technical field of agricultural surveying and mapping engineering, and the method comprises the following steps: building a field weed marking data set comprising a plurality of crop types and each phenological stage, and dividing the data set in proportion to provide a reference for model training and verification; on the basis of the YOLOv10n network model, constructing a field weed detection model and training the field weed detection model; the model performance is improved by introducing a feature learning optimization mechanism, and the field weed detection precision is improved while the light weight of the model is kept; according to the method, a field weed image data set of various crop types and various phenological stages is constructed to provide training and verification references for the model, and then a lightweight network architecture is introduced to fuse modules such as multi-scale cavity convolution and an attention mechanism to optimize and improve the model, so that the recognition precision is ensured, the calculation cost is reduced, and the accuracy of the model is improved. And then deploying to a development board to build a four-wheel drive type field moving weed detection system.

The invention relates to the technical field of agricultural equipment, and discloses a peanut film mulching hole-free seeder which comprises an integrated frame, and a connecting frame is arranged at the front end of the integrated frame and used for being connected with a traction machine in a hanging mode; according to the device, a weakening point is accurately preset on a mulching film through a liftable micro hot needle, the traditional manual or mechanical hole breaking is replaced, a hole-free seeding mode of one hole and one point is realized, and the hole-free seeding efficiency is greatly improved. According to the method, the complex procedure of manual film breaking is avoided, the labor intensity is reduced, through the controllable design of the weakening points, it is ensured that peanut seedlings naturally break the mulching film, damage to roots is reduced, the emergence rate and the survival rate are increased, and meanwhile the problem of weed breeding or soil moisture loss caused by too large broken holes is effectively solved.

The invention discloses a walking type mower, relates to the field of mowers, and solves the problem that an existing walking type mower is difficult to weed deep gullies, the walking type mower comprises a protective shell and a holding rod mounted on the outer side of the protective shell, a mounting disc is mounted at the bottom of the protective shell, and a driving motor is mounted at the top of the mounting disc; a rope disc is arranged at the bottom of the mounting disc, and two grass trimming ropes are arranged on the outer side of the rope disc; the grass trimmer further comprises a rope pressing mechanism used for adjusting the length of the grass trimming rope, the rope pressing mechanism is installed on the outer side of the installation disc, the rope pressing mechanism comprises a pressing ring arranged on the outer side of the rope disc, and the pressing ring can press and contract the length of the grass trimming rope; by means of the rope pressing mechanism, an eccentric wheel can press a pressing ring downwards, the pressing ring abuts against the outer side of a grass trimming rope, the grass trimming rope swings downwards, the length of the grass trimming rope is adjusted, a rope disc can treat weeds in a gully through the grass trimming rope, and therefore the effect of gully weeding is achieved.

The invention relates to the technical field of crop cultivation, in particular to a red bean plastic film mulching water-saving and fertilizer-retaining cultivation method, and aims to solve the problems that the integrity of an existing plastic film is damaged, the utilization efficiency of water and fertilizer is low, and the labor intensity of field management is high. The cultivation method is characterized by comprising the steps of soil preparation and bedding, base fertilizer application, laying of a drip irrigation belt below the center line of a ridge body, laying of a mulching film with prefabricated holes, accurate guiding of red bean seeds into the holes of the mulching film through a precision seeder, water and fertilizer integrated accurate management in the whole process, and prevention and control of diseases, weeds and weeds in combination with green prevention and control measures. By adopting the technical scheme, the utilization efficiency of water resources and fertilizers can be remarkably improved, stable performance of the mulching film is ensured, the labor intensity is reduced, and the effects of saving water, retaining fertilizers, saving labor, increasing yield and protecting environment are achieved.

The invention is suitable for the field of farmland weed treatment, and provides an unmanned vehicle-mounted remote sensing detection-based farmland early weed identification method, which comprises the following steps of: constructing an unmanned vehicle-mounted remote sensing detection system and acquiring a multispectral image, a thermal infrared image and GPS (Global Positioning System) data; data transmission and preprocessing of the multispectral image and the thermal infrared image are carried out; carrying out weed recognition on the preprocessed image by using a weed recognition algorithm; experiments prove that the weed recognition result method greatly improves the monitoring efficiency, is especially suitable for large-scale farmland, reduces herbicide abuse, and gives consideration to both production benefits and environmental protection. The technology can effectively reduce resource competition between crops and weeds, directly improve the yield and quality, and reduce manpower and pesticide cost at the same time. And the data can be integrated into a precision agricultural system to promote the implementation of technologies such as variable fertilization and intelligent irrigation. In addition, the flexibility of the unmanned system enables the unmanned system to adapt to complex terrains, and a new solution is provided for organic agriculture and breeding test fields.

The utility model relates to the field of gardening tools, and particularly discloses a grass trimming head and a grass trimmer, and the grass trimming head comprises a machine shell, a motor installed in the machine shell, a grass trimming head assembly installed on one side of the machine shell and provided with rotation driving force by the motor, a motor support arranged on one side of the machine shell, and a separator arranged between the machine shell and the grass trimming head assembly. The rotating piece is installed on the inner side of the isolation piece, the isolation piece is a fan blade cover, the rotating piece is fan blades, a cutting piece is installed on the fan blade cover, and the isolation piece can guide heat dissipation airflow and can isolate and protect the fan blades at the same time. An air outlet is formed in the fan blade cover, and in the rotating process of the fan blades, air flow generated by the fan blades can be blown out from the inner side of the isolation piece to the outer side of the isolation piece through the air outlet; in the rotating process of the fan blades, the cutting piece can cut weeds on the inner side of the isolation piece, the cut weeds can be blown to the outer side of the isolation piece through airflow generated by the fan blades, and the problem that the weeds enter the grass trimming head to be wound around a line can be effectively solved.

The weed removing robot provided by the embodiment of the application comprises a body, a first driving mechanism, a second driving mechanism and a grass removing mechanism. The weed removing robot provided by the embodiment of the application controls the position of the second driving mechanism through the first driving mechanism, so that the trigger and the grass removing mechanism are connected or disconnected, thereby controlling the grass removing mechanism arranged on the second driving mechanism, making the grass removing mechanism perform the rope releasing operation or the grass removing operation, and then after the grass removing operation is completed, the grass removing mechanism is detached from the output shaft of the second driving mechanism, and the grass cutting knife is installed on the output shaft, so that the grass cutting operation can be realized, and the grass cutting and the grass removing operation can be realized without using multiple devices.

The present invention discloses a photovoltaic panel and a supporting device thereof, belonging to the technical field of photovoltaic panels, comprising a photovoltaic panel main body, a supporting frame fixedly installed on the lower side of the photovoltaic panel main body, a supporting column fixedly connected to the bottom of the supporting frame, a rotating frame rotatably connected to the support column, an adjusting mechanism rotatably connected to the rotating frame, a protective cover fixedly connected to the adjusting frame, a weeding mechanism rotatably connected to the bottom end of the adjusting frame, and a movable frame slidingly fitted in the adjusting frame; in the present invention, during the use of the photovoltaic panel, when the weeds below the photovoltaic panel grow to a certain height, the rotating rotating frame can be used to drive the knife rod to rotate, so that the knife rod automatically weeds the weeds below the photovoltaic panel, ensuring that the light in the area below the photovoltaic panel is not blocked, reducing its potential threat to the photovoltaic system, ensuring the long-term stable operation of the photovoltaic equipment, maintaining the power generation efficiency of the photovoltaic panel, reducing labor maintenance costs, and improving the overall management level.

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The invention discloses a weed feature recognition and weeding cutter switching system and method, and the system comprises a multi-source sensing fusion sensing layer, a deep learning algorithm decision-making layer and a cutter replacement execution layer, and a sensing end comprises a multispectral camera, a high-definition camera, a depth camera and an environment sensor. Features of weeds can be acquired in multiple dimensions and positioning can be realized, and a deep learning algorithm realizes input of a multi-source sensing fusion spliced image and output of a cutter correspondingly used by the weeds and a specific position of the cutter. According to the system, multi-dimensional characteristics of weeds are collected through a sensing layer multi-source sensor, a decision-making layer adopts a YOLOv8s algorithm to perform data fusion and weight distribution, a tool interaction model is dynamically constructed in combination with a DDPG algorithm, finally, an execution layer intelligently switches tools according to a decision-making instruction and generates an optimal weeding path, full-process automatic precise operation is achieved, and the efficiency is improved. The efficiency is improved, the adaptability is enhanced, and the practical value is great.

A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.

The invention discloses a conyza sumen biocontrol bacterium and application thereof, and relates to the technical field of weed biological control, the conyza sumen biocontrol bacterium has a strain number of SMBJC-3, is preserved in the China Center for Type Culture Collection, the preservation time is September 17, 2025, the preservation number is CCTCC NO: M 20252054, and the conyza sumen biocontrol bacterium is classified and named as Colletotrichum orbiculare. The conyza sativa biocontrol bacterium provided by the invention aims to provide a new resource and method for biological control of conyza sativa.

The utility model relates to the technical field of landscaping, and discloses a landscaping weeding device which comprises a bottom plate, and a weeding mechanism and a grass collecting mechanism are arranged on the bottom plate. The weeding mechanism comprises a frame body, the frame body is fixedly connected to the top of the bottom plate, and the frame body is arranged in an inverted U shape. The motor drives the rotating shaft to rotate, the rotating shaft drives the weeding blades to rotate to cut and remove weeds, the bidirectional threaded rod rotates to drive the moving block to horizontally move under limiting of the limiting sliding rod, and the moving block drives the hinge arm to rotate around the hinge point, so that the hinge arm pushes the mounting plate; the mounting plate drives the motor, the rotating shaft and the weeding blade to vertically move, so that the height of the weeding blade can be freely adjusted, the weeding flexibility is improved, and weeding is more thorough.

The invention provides a shrub and grass matched wind prevention and sand fixation method under a photovoltaic panel of a mudshandack sand land. The method comprises the following steps: S1, planting herbaceous plants matched with small half shrubs in front of the photovoltaic panel; s2, adopting sand-barrier automatic sowing equipment to plant shrubs and weeds in and behind the photovoltaic panel; the herbaceous plants comprise artemisia desertorum, agriophyllum squarrosum, liquorice, wheatgrass, leymus chinensis and astragalus mongholicus in North China; the small half shrubs are caragana microphylla, caragana angustifolia and camellia hupehensis; the shrubs are red sand, caragana microphylla, caragana angustifolia, amygdalus pedunculata, alpinia hupehensis, salix babylonica, artemisia stelleriana, caragana microphylla and thyme; the weeds are agriophyllum squarrosum, agriophyllum squarrosum, pugionium Wind prevention and sand fixation engineering and photovoltaic industry land are ingeniously combined, adaptive vegetation is planted under the photovoltaic panel which is most prone to wind erosion and in the peripheral area, surface soil can be effectively fixed, sand dune mobility is reduced, a photovoltaic panel support foundation is protected against wind erosion damage, and the service life of a power station is prolonged.

The invention belongs to the technical field of agricultural machinery and intelligent control, and particularly relates to an intelligent precise weeding robot based on multispectral recognition. The robot aims at solving the problems that in the prior art, due to visible light morphological recognition, the forms of crops and weeds are similar, and the recognition precision is low and the robustness is poor in the complex field environment. The robot comprises an autonomous mobile platform and a sensing operation assembly, the sensing operation assembly integrates a multi-mode sensing unit, a data fusion and decision core and a high-energy thermal ablation module, and the multi-mode sensing unit provides eight narrow-band pulse light with specific wavelengths through an active light source illumination module. Realizing synchronous acquisition of spectrum and space information by combining multispectral imaging and three-dimensional profile measurement; the data fusion and decision core performs registration, segmentation and classification on multi-source data to generate weed target coordinates; and the high-energy thermal ablation module implements 1470-nanometer laser accurate ablation based on the coordinates, and verifies the operation effect in real time through coaxial infrared thermal imaging. By means of the technical scheme, high-confidence-coefficient distinguishing and millimeter-level lossless precise weeding of crops and weeds in the complex farmland environment can be achieved.

Compounds of Formula (I) wherein the substituents are as defined in claim 1. The invention further relates to herbicidal compositions which comprise a compound of Formula (I) and to the use of compounds of Formula (I) for controlling weeds, in particular in crops of useful plants.

The utility model belongs to the technical field of rail transit cleaning, and relates to a movable spraying weeding device for rail transit, which comprises a vehicle body, a pesticide storage box, a fixed spraying mechanism and a movable spraying mechanism, the vehicle body moves along a track line; the pesticide storage box is fixed on the vehicle body; the fixed spraying mechanism is communicated with the pesticide storage box; the movable spraying mechanism is communicated with the pesticide storage box; in the moving process of the vehicle body, the fixed spraying mechanism sprays weeding agents to weeds close to the track, the movable spraying mechanism sprays weeding agents to weeds far away from the track, the fixed spraying mechanism and the movable spraying mechanism are matched with each other, it is guaranteed that the weeding agents cover the weeds comprehensively without dead corners, spraying blind areas are avoided, and the weeding efficiency is improved. The weeding effect is improved; meanwhile, by adjusting the height and angle of the movable spraying mechanism, the coverage range of the weeding agent is dynamically adjusted according to the growth condition of the weeds, it is further ensured that the weeding agent effectively covers most weeds, and the weeding effect is improved.

Convection interactions like exposure of seeds to hot ozonated air, hot humidified air, hot ozonated humidified air and optional ultrasound—obtain higher net reduction of germination viability (0-1 percent germination versus 70 percent obtained for a control) of seeds than that obtained via a seed illumination process alone that uses Medium Wavelength Infrared and an Indigo Region Illumination Distribution.

The invention discloses a corn seedling and weed detection method and device based on a space-spectrum collaborative network. The method comprises the following steps: obtaining corn seedling and weed images in a natural farmland environment to construct a data set, and marking the data set; performing data enhancement on the annotation data set, and dividing the annotation data set into a training set, a verification set and a test set; a corn seedling and weed detection model is constructed based on a YOLOv11 architecture, and the model comprises a feature distribution unit, a spatial structure feature extraction branch, a spectrum context modeling branch, a space-spectrum feature fusion unit, a neck network unit and a detection head unit. Initializing network parameters of the detection model, designing a BCG-WIoU bounding box regression loss function, and training the detection model by using a training set; and reasoning the input image by using the trained detection model, and outputting the category and position information of the corn seedlings and weeds.