51 results about "Weed detection" patented technology

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 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 discloses a weed detection method and device used in a complex environment background, and the method comprises the steps: carrying out the simplification of channel attention convolution and channel self-attention convolution in a smooth U-shaped network; a space channel convolution attention module is designed by introducing a space attention branch, a gating mechanism and two different residual connection modes, an edge filtering and Gaussian filtering combined mechanism is proposed based on an edge Gaussian aggregation module in a linear efficient Gaussian network, and rapid spatial pyramid pooling is fused. A multi-scale pooling strategy, a channel attention mechanism and a dynamic weight distribution mechanism are introduced, a multi-scale pyramid pooling hyper-edge Gaussian aggregation module is designed, a context consistency strategy is constructed on the basis of a space enhancement feedforward module in a space enhancement multi-scale network, dynamic gating is designed through shielding masks, and a multi-scale pyramid pooling hyper-edge Gaussian aggregation algorithm is constructed. Designing an efficient multi-scale space enhancement network module; based on a YOLOv9t model, a space channel convolution attention module, a multi-scale fast pyramid pooling hyper-edge Gaussian aggregation module and an efficient multi-scale space enhancement network module are combined to construct a space enhancement type YOLO model; and weeds are detected based on the spatial enhanced YOLO model.

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.

The invention relates to a paddy field weed detection method and system, and relates to the technical field of agricultural intellectualization, and the method comprises the steps: obtaining image data of rice seedlings, weeds and paddy field backgrounds under different spectrums; annotating the image data, and preprocessing the annotated image to obtain preprocessed data; constructing a cascaded encoder-decoder network, and performing seedling detection on the preprocessed data image to obtain a detection result; according to a detection result, extracting a paddy field boundary to obtain a field ridge boundary line; performing semantic segmentation on the paddy field image according to the field ridge boundary line to obtain segmentation masks of seedlings and weeds; and identifying the distribution condition and the type of the weeds by analyzing the segmentation masks so as to obtain the initial distribution condition of the weeds. The intelligent level of paddy field weed management can be improved, the labor cost is reduced, the rice production efficiency is improved, meanwhile, environmental protection is facilitated, and agricultural sustainable development is promoted.

A method, a system and an electronic device for detecting weeds in farmland are provided, wherein the method includes: collecting a target image of weeds in farmland; constructing a weed detection model by using YOLOv8 based on a RevColNet backbone network, and identifying weeds based on the weed detection model; accurately removing the weeds. The method is used for solving the defects that: when identifying weeds in farmland, all kinds of information of weeds cannot be well described, it is difficult to obtain high identification accuracy, and problems such as high computational complexity, large model parameters, large model scale and the like are faced. The method and the system provide an improved model based on YOLOv8, which can identify weeds in farmland with higher accuracy, with lower computational complexity, and higher weed identification efficiency.

This invention relates to the field of agricultural intelligent equipment and machine vision technology, and discloses a method for weed detection and growth point localization based on shared features. The method constructs a collaborative dataset containing weed detection labels and growth point labels, utilizes a shared feature extraction network to extract multi-scale features, and sets detection task branches and growth point localization branches in a unified feature space to achieve joint modeling of weed detection and growth point localization. Then, adaptive positive sample selection is performed based on the joint cost between candidate sample points and labeled growth points, and joint training is conducted using detection loss, growth point localization classification loss, and growth point localization regression loss. During the inference stage, the method outputs weed detection results and growth point localization results, and generates target point information. This method can reduce redundant calculations and error cascading in the multi-stage processing, and improve the accuracy of growth point localization and the stability of target point output in complex farmland scenarios.

The invention discloses a lightweight field weed detection method and device, computer equipment, a storage medium and a program product, and relates to the field of computer vision. The method comprises the steps that a weed detection model is constructed and comprises a backbone network, a neck network and a detection head, a dynamic sampling enhancement module is introduced into the backbone network and used for conducting self-adaptive adjustment on the sampling position of a convolution kernel in the backbone network, and a cross-layer guide aggregation module is introduced into the neck network and used for conducting two-way input based on high-level features and low-level features and conducting self-adaptive adjustment on the sampling position of the convolution kernel in the backbone network; performing explicit guidance on low-level details in the bottom-level features by using high-level semantics in the high-level features, and fusing the high-level features and the low-level features by using difference information between the high-level features and the bottom-level features; and inputting the image data of the farmland scene into the weed detection model, and identifying and outputting weed types. According to the scheme, effective balance between light weight and high performance is realized, and a stable, rapid and accurate weed detection result can be provided in a complex farmland scene.

The invention discloses a farmland weed detection and variable pesticide application method and system, and relates to the field of agricultural intellectualization, and the method comprises the steps: obtaining a farmland visible light image; inputting the farmland visible light image into an improved YOLOv7 model to obtain a weed bounding box and a weed density matrix; the weed density matrix comprises weed density values of a plurality of regions; and establishing a nonlinear mapping relationship between the weed density value and a pre-applied pesticide level set, and performing variable pesticide application on the farmland based on the weed density matrix and the nonlinear mapping relationship. According to the method, the recognition precision of the weeds in the seedling stage and the pesticide application precision are improved, and the utilization rate of the pesticide is increased.

The present invention relates to a fixed-point weed spraying system for implementing a fixed-point weed spraying method based on a classification value generated from a sub-region image by a processor applying a machine learning-based training model to the sub-region image, the system comprising: a camera; the nozzle assembly comprises a nozzle; and a processor. A method implemented by the system includes acquiring, by a camera, a full-view region image of a crop surface. The method further includes extracting, from the full-field-of-view region image, a sub-region image corresponding to a nozzle positioned to provide a spray region extending over a portion of the crop surface depicted in the sub-region image; generating, by a processor, a classification of the sub-region images according to a training model based on machine learning; and selectively activating the nozzle according to the classification of the sub-region image.

The present application relates to a kind of weed classification detection method and system based on YOLOv8 improved algorithm.The method comprises: the image to be detected containing weed is input to YOLOv8 improved algorithm model, the image data set after processing is input to the backbone network of YOLOv8 improved algorithm model, different scale image feature information is extracted by C2f-FADC module and input to Neck network, DASI module carries out low resolution high semantic information and high resolution low semantic information fusion, produces multi-scale semantic feature information and is transmitted to task alignment detection head, extracts different task interaction features, calculates classification features, obtains weed classification result.FADC module is used to replace Bottleneck module in C2f module, C2f-FADC module is proposed, and the module is integrated into backbone network, which can improve the receptive field of convolution;DASI module can well enhance the accuracy of small target detection by adaptive selection and fine fusion of high and low dimensional features, improve the performance of weed detection model.

The application discloses a dynamic adaptive vehicle-mounted multispectral farmland weed detection method and relates to the field of agricultural information technology.The application is aimed at solving the problems of large calculation amount and difficulty in application to actual weed detection of the existing farmland weed detection method.The application comprises the following steps: obtaining a rough control interval description object by using a current longitude, a current latitude, a time-effect speed mean variable, a time-effect speed change variable, a time-effect positioning minimum deviation variable, a time-effect positioning maximum deviation variable and a multispectral image; constructing a regional rough control interval list by using the rough control interval description object; obtaining a difference evaluation index of two rough control interval description objects; and obtaining a farmland weed detection result based on the difference evaluation index of the rough control interval description object.The application is used for identifying weeds in farmland.

The invention discloses a rice field weed recognition analysis method and system based on image recognition, and relates to the technical field of rice field weed recognition, and the method comprises the following steps: obtaining a first rice gray threshold and a second rice gray threshold based on a first number of rice images in seedling stages; based on the paddy field grey-scale map, the first paddy rice grey-scale threshold value and the second paddy rice grey-scale threshold value, obtaining a paddy rice binary image; obtaining a binary rotation image based on the rice binary image; constructing a first coordinate point to an f-th coordinate point based on the binary rotation graph; screening the first coordinate point to the f-th coordinate point to obtain a first screening coordinate point to an f-th screening coordinate point; based on the first screening coordinate point to the f-th screening coordinate point, judging whether an abnormal plant area appears or not; the method is used for solving the problem that the whole recognition process is relatively tedious and complicated as a proper weed detection method cannot be set based on the rice growth environment in the rice field weed recognition technology.

The application discloses a rice field weed identification analysis method and system based on image recognition, and relates to the technical field of rice field weed identification, and comprises the following steps: obtaining a first rice grayscale threshold value and a second rice grayscale threshold value based on a first number of rice seedling stage images; obtaining a rice binary image based on a rice field grayscale image, the first rice grayscale threshold value and the second rice grayscale threshold value; obtaining a binary rotation image based on the rice binary image; constructing a first coordinate point to an fth coordinate point based on the binary rotation image; screening the first coordinate point to the fth coordinate point to obtain a first screened coordinate point to an fth screened coordinate point; and judging whether an abnormal plant area appears based on the first screened coordinate point to the fth screened coordinate point; the application is used to solve the problem that the rice field weed identification technology cannot set a suitable weed detection method based on the rice growth environment, resulting in a relatively cumbersome and complex overall identification process.

The application discloses a kind of based on hypergraph enhanced YOLOv11 frame weed detection method and device, method includes: based on YOLOv11 baseline network, reconfigures local-global collaborative main network, obtains first optimization network reconfiguration neck network is hypergraph enhanced semantic neck, to establish high-order space topological feature fusion mechanism, obtains second optimization network;Second optimization network is used as the final enhanced detection model, and based on the enhanced detection model, weeds in field are detected;Using the constructed data set and multiple public data sets, the enhanced detection model is comprehensively experimentally verified;The enhanced detection model after verification is used for weed detection.The device includes: processor and memory.The present application solves the problem that the weed detection model in the prior art has low detection accuracy due to insufficient global context perception, weak geometric deformation representation capability and missing high-order topological relationship in complex farmland scenes.

The invention provides a lightweight corn-weed accurate detection algorithm based on improved YOLOv10s, and the algorithm comprises the steps: collecting corn seedling and weed images, and constructing a corn field weed data set; an improved lightweight corn-weed detection model of YOLOv10s is constructed, a D-PP-HGNet lightweight network is constructed to replace an original trunk part of the YOLOv10s model, and an improved RCS-OSA module is introduced into a neck network. Using the corn field weed data set to train and evaluate the improved lightweight corn-weed detection model of YOLOv10s, and using the improved lightweight corn-weed detection model of YOLOv10s after training and evaluation to perform classification detection on to-be-detected corn seedlings and weed images. The technical problems that an existing algorithm is low in recognition accuracy, high in calculation intensity and difficult to deploy at a mobile terminal are solved.

The invention relates to a paddy field weed detection method, device and equipment based on unmanned aerial vehicle low-altitude remote sensing and a medium, and the method comprises the steps: inputting a paddy field low-altitude remote sensing image obtained by an unmanned aerial vehicle platform, and extracting a multi-scale feature map containing shallow texture information and deep semantic information at the same time; inputting into a cavity space pyramid pooling module for processing to obtain a feature map set; inputting the feature map set into a feature refining module, and performing iterative feature refining and result prediction through a cascade residual decoding and gating guide attention module; and inputting the prediction result graph and the high-resolution feature graph in the set into a feature fusion module, performing feature fusion and generating a prediction response graph, and restoring the prediction response graph to the spatial resolution consistent with the original input image through up-sampling to obtain a final detection result graph. According to the method, the representation of the target area can be continuously improved on the basis of initial rough positioning, so that the detection accuracy of weak and camouflage weed targets is remarkably improved.

The application discloses a kind of based on improved yolov8 network's vegetable field weed model construction method, comprising: S1: collection vegetable field weed image, constructs vegetable weed data set, and vegetable weed data set is divided into training set, verification set and test set according to preset proportion;S2: improved yolov8 network model is constructed to establish vegetable field weed detection model;S3: training set is input to the improved yolov8 network model and is trained, and the performance of model is verified using verification set and the parameters of model are adjusted according to the evaluation result of verification set, finally the performance of model is evaluated using the test set, and the final vegetable field weed detection model is obtained;In the step S2, it includes: S21: depth separable convolution DSConv is introduced in original yolov8 network model;S22: the Concat module in neck layer is replaced by context interaction fusion module CIFM;S23: Head detection head is replaced by lightweight LSCD detection head.The application realizes the quick and accurate detection of vegetable and weed.

The invention discloses a method for visually and rapidly detecting Ile-1781-Leu mutation of a moleplant seed ACCase gene, and relates to the technical field of herbicide-resistant weed detection. Specific primers (SEQ ID NO.1 and SEQ ID NO.2) and a probe (SEQ ID NO.3) for RAA reaction are designed on the basis of a 1781st amino acid resistance mutation region of a moleplant seed ACCase gene, a CRISPR / Cas12a fluorescent color development system is combined, an RAA-CRISPR / Cas12a visual detection method is established, a sample with Ile-1781-Leu mutation shows fluorescence under a blue light instrument, and the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that Compared with a traditional PCR method, the method is easy to operate, high in sensitivity and short in reaction time, supports visual rapid detection, and provides technical support for rapid diagnosis and scientific prevention and treatment of resistant moleplant seed target mutation.

The invention provides a multi-dimensional feature fusion winter wheat weed detection method and system, and relates to the field of agricultural information technology and precision agriculture technology, and the method comprises the steps: constructing a deep learning model of WMGNet, the deep learning model of the WMGNet comprises a wavelet feature processing module, a bispectrum Mamba branch module, a graph volume integral branch module, a feature fusion module and a classifier which are connected in sequence; inputting the standardized winter wheat image into a deep learning model of WMGNet; extracting a multi-scale feature map of the standardized winter wheat image through a wavelet feature processing module; through a bispectrum Mama branch module, extracting long-range dependency features of the multi-scale feature map; non-local features of the enhanced features are extracted through a graph volume integral branch module; fusing the long-range dependency feature and the non-local feature to obtain a fused feature; and inputting the fusion features into a classifier, and outputting a detection result of the winter wheat high-resolution hyperspectral image.

The invention discloses a power terminal weed detection method, device and equipment and a storage medium, and relates to the technical field of power equipment maintenance. According to the method, the feature information in the to-be-recognized weed image is acquired, and then the weed feature image is generated, so that information loss in the feature extraction process of the to-be-recognized weed image is avoided, and the integrity of feature expression is enhanced; according to the method, the importance and the identification degree of key features in the weed feature image can be kept in the processing process in a long residual connection processing mode, and the gradient explosion problem of a weed identification model in the weed feature image processing process is relieved; according to the method, the global mode and the local details can be paid attention to when the weed feature image is processed by the weed recognition model, and the over-fitting risk is reduced, so that the recognition accuracy of the weed recognition model on the weed feature image is improved, and the accuracy of power terminal weed detection is further improved.

The invention discloses a field weed growing point detection model and method based on key points. Backbone is used for receiving a field weed image to realize learning of multi-scale weed image features and outputting feature maps of different stages; the Neck uses multi-scale fusion to fuse different stages of feature maps output by the Backbone, and the performance ability of weed features is enhanced; and the Head converts the feature map output by the Neck into specific information required by weed detection, wherein the specific information comprises category, position and confidence information of weeds. The constructed detection model can maintain high detection precision in a natural field environment, the model is light, the real-time detection capability is improved, and the growth points of weeds can be accurately identified in real time in the field environment. By adopting the key point detection method, the specific position coordinates of the growth points of the weeds can be directly output, and the problem that the traditional weed detection method can only detect the whole or part of the weeds and is difficult to determine the growth points is solved.

The invention discloses a variable pesticide application system and a field weed detection and variable pesticide application method, and belongs to the technical field of intelligent agricultural machinery equipment. The variable pesticide application system is provided with a control assembly and two damping type camera assemblies with freely adjustable angles and heights; according to the field weed detection and variable pesticide application method, a weed detection model is based on YOLOv8n, CBS and C2f in a backbone network and a neck network are replaced with GhostCBS and C2fGhost respectively, a feature fusion module is replaced with an optimized weighted bidirectional feature pyramid fusion network BiFPNContact2, Triplet Attention is introduced before Desection 3, and Wise-IoU v3 is used as a loss function; the invention further comprises an image division and calibration method and a variable pesticide application method. According to the invention, the camera shooting stability and flexibility and the variable pesticide application fineness and accuracy can be improved.

This invention relates to the field of farmland remote sensing image processing and recognition technology, specifically to a sugarcane field weed detection device and system based on UAV multispectral imaging. The system includes a data acquisition module, a feature generation module, a weed discrimination module, and a verification output module. The data acquisition module acquires multispectral images of the sugarcane field, ambient light parameters, and UAV positioning attitude parameters, and outputs these to the feature generation module. The feature generation module performs registration and correction based on the multispectral images, ambient light parameters, and UAV positioning attitude parameters, extracts sugarcane planting row information, and generates a normalized feature package using the sugarcane canopy reference area before outputting it to the weed discrimination module. This invention, through its data acquisition module, feature generation module, weed discrimination module, and verification output module, solves the problems of poor weed detection stability, high false positive rate, and inconvenient verification of detection results in sugarcane fields under conditions of varying light intensity, soil background differences, and similarity between sugarcane and weeds.

The embodiment of the invention provides a method and equipment for detecting weeds in a fence and a medium. The method comprises the following steps: receiving a to-be-identified image and a shooting position which are sent by shooting equipment and are obtained by shooting a to-be-identified fence; obtaining a target historical image shot at a shooting position from a plurality of historical images obtained in advance, and performing feature alignment on the to-be-recognized image and the target historical image to obtain an aligned to-be-recognized image; performing foreground detection on the aligned to-be-recognized image by adopting a background model corresponding to the shooting position to obtain a foreground region; and carrying out weed detection on the foreground region by adopting a weed detection model to obtain a detection result. The method is used for achieving the effect of improving the detection accuracy and robustness of the weeds in the fence.

The invention relates to a cotton field targeted weeding path planning method based on a weed hazard thermodynamic diagram, which comprises the following steps: realizing cotton field weed detection based on an improved target detection model YOLOv8n, and extracting weeds in a high-resolution remote sensing image by combining a slice-assisted reasoning technology; the invention discloses a crop row feature point extraction method based on a least square method. Cotton field rows and cotton field ridge center lines are obtained; and obtaining a weeding route through different weeding thresholds based on the central line of the cotton field ridge and the weed hazard thermodynamic diagram. The deep learning technology and the GIS technology are combined to generate a weed hazard thermodynamic diagram, a weeding route map meeting different weeding standards, a collaborative decision of a robot weeding path and an operation mode is formed, a low-efficiency traversal type single operation mode is broken through, a weeding mode is dynamically allocated, for example, a high-threat weed region is preferentially targeted, and the weeding efficiency is improved. The optimal operation mode is selected by preferentially selecting the highest efficiency, preferentially selecting the weeding area and the like, and efficient targeted weeding can be effectively achieved.

The invention discloses a farmland weed detection system and detection method based on image recognition, and the method comprises the steps: receiving crop sowing data, dividing a farmland into a crop region and a discrimination region through a region division unit, marking the crop coordinates of the crop region and the discrimination region in static image recognition data, the lateral blowing module is used for laterally blowing the marked crops to obtain dynamic image identification data of the crop area and the discrimination area, the first weed marking module is used for marking the weeds in the discrimination area, and the second weed marking module is used for marking the weeds exposed in the crop area; shielding weeds in a crop area and a discrimination area are marked through a dynamic image weed marking module based on dynamic image recognition data, the types of the weeds are specifically discriminated according to a judgment index PDZs, features of the exposed weeds in a wind blowing motion state are acquired, and the weeds are judged and marked.

The application discloses a kind of weed detection methods based on improved YOLOv11 model, including the following steps: improved YOLOv11 model is constructed, the model is integrated in the feature path of the detection head at the end of main network and high-efficiency multi-scale attention module and high-efficiency multi-scale channel attention module, to form the feature extraction and fusion mechanism of double attention enhancement;Prepare farmland weed image dataset, and the dataset is labeled and enhanced preprocessing;The improved YOLOv11 model is trained using the dataset, and a trained weed identification model is obtained;The farmland image to be detected is input into the trained weed identification model, and the class and position information of weed are output.The weed detection method improves YOLOv11 model by introducing double attention mechanism, to improve its detection precision and robustness to weed in complex farmland environment, while maintaining efficient real-time inference ability.