45 results about "Plant identification" patented technology

A plant recognition method and related devices. The plant recognition method includes: obtaining a plant image and question text about recognizing the plant in the plant image; inputting the plant image and the question text to a plant recognition model, the plant recognition model includes a first visual model and a multimodal large language model, the first visual model is configured to receive the plant image to extract first image features of the plant image, the multimodal large language model is configured to receive the first image features and the question text to recognize the plant in the plant image, the plant recognition model is trained with multimodal data, the multimodal data includes plant images, questions about recognizing plants in the plant images, and answers to the questions; and outputting answer text provided by the plant recognition model about recognizing the plant in the plant image.

The invention relates to a parameter control method and device of a plant growth cabin, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a plant identifier and a corresponding growth stage of a plant in a plant growth cabin, weather prediction data and current environment data collected by different environment sensors in the plant growth cabin; determining expected environment data of the plant growth cabin according to the plant identifier and the growth stage; generating an initial control strategy of the plant growth cabin according to the difference between the expected environment data and the current environment data; generating environment change prediction data of the plant growth cabin according to the weather prediction data; and according to the environment change prediction data, adjusting the initial control strategy to obtain a target control strategy. By adopting the method, the intelligent level of environment regulation and control in the plant growth cabin can be effectively improved.

A plant marker with integrated insect monitoring capability. The plant marker includes a base layer having a rectangular segment and a pointed segment configured for soil insertion. The rectangular segment includes an upper surface coated with adhesive material for capturing insects and an underside surface formed of markable material for receiving plant identification information. A removable liner covers the adhesive material prior to use. The device enables simultaneous plant identification and pest assessment.

The invention discloses a minimum population wild plant identification and early warning method and system. The system comprises: an environmental DNA monitoring network unit, which analyzes environmental DNA through automatic sampling, evaluates the state of species and triggers a first early warning; the biological voiceprint monitoring and recognition unit is used for monitoring related acoustic activities and illegal intrusion behaviors of the target species by using intelligent recording nodes deployed in a gridding manner and a voiceprint recognition model, and triggering second early warning; and the multi-source information fusion and causal inference unit is used for integrating the environment DNA, the voiceprint and the meteorological data, analyzing the causal relationship among the ecological factors based on a causal inference model, generating protection measures and outputting comprehensive early warning. According to the invention, all-weather, interference-free and precise monitoring of the extremely small population wild plants is realized, the problems of low efficiency, many blind areas, large interference and the like of a traditional monitoring mode are solved, and effective technical support is provided for protection of rare plants.

The invention relates to the technical field of tobacco plant identification and counting, and discloses an unmanned aerial vehicle tobacco plant identification and counting method and system based on improved deep learning, which combines technologies of image classification, image segmentation, feature point extraction, machine learning, target detection and the like with refined intelligent identification of tobacco. According to the method, high-precision image recognition and analysis are realized by utilizing the efficient feature extraction and calculation capability of the method, point plant counting can be accurately performed, the efficiency and precision of agricultural production are greatly improved, the problem of calculation complexity of a traditional method in large-scale high-resolution image processing is solved, and powerful technical support is provided for precision agriculture. Therefore, the invention provides an unmanned aerial vehicle tobacco plant identification and counting method based on improved deep learning target detection and multi-dimensional post-processing, and high-precision and robust detection and counting of tobacco plants in large-scale tobacco field orthoimages are realized by constructing a three-stage assembly line of image preprocessing, improved target detection and multi-dimensional post-processing.

The application discloses a plant specimen identification method, and relates to the technical field of plant identification, which comprises the following steps: extracting multi-dimensional data information in a plant specimen image, performing feature recognition on the extracted multi-dimensional data information, and obtaining corresponding multi-dimensional data features; performing feature coding based on the obtained multi-dimensional data features, obtaining a corresponding feature matrix, performing feature fusion on the obtained feature matrix, and obtaining corresponding multi-modal features; performing feature classification on the obtained multi-modal features by using a classification model, comprehensively deciding a classification result, and obtaining an identification result of the plant specimen; the application effectively fuses data of two different modes of images and texts, overcomes the problem of limited single-mode identification information, and can significantly improve the automation level and accuracy of plant specimen identification.

A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

The utility model belongs to the technical field of plant identification, and particularly relates to a simple number plate for trees, which comprises a mounting plate, a fixing plate and a fixing plate. The inner walls of the two sides of the containing groove are each provided with a clamping groove, and a limiting inclined plate is further arranged outside each clamping groove. The rear end of the limiting inclined plate is fixed on the rear wall plate of the accommodating groove, and the front end is provided with an inclined surface; the number plate main body comprises a first plate body and a second plate body; the first board body is located in the containing groove, and the two sides of the first board body are each provided with a first guide groove. A buckling piece is arranged in the guide groove I, and is matched with the clamping groove in the corresponding position; the second board body is located at the front end of the first board body, and two sides of the second board body exceed two sides of the first board body. The utility model can solve the problem that the number plate is inconvenient to take out and replace due to too tight insertion in the mode of inserting and fixing the number plate in the prior art, and has a better market application prospect.

The application relates to the technical field of base station survey, and discloses an intelligent terminal automatic survey data analysis and processing method and system based on deep learning, which comprises a server, an intelligent terminal and a monitoring device; the server is used for receiving base station information corresponding to each base station collected by the monitoring device; the intelligent terminal comprises a data acquisition module, a data calling module, a plant identification module and a processing module; the processing module is used for determining the base station influence degree of main plants corresponding to each base station according to surrounding environment data corresponding to each base station, plant standard growth demand information, base station basic information and base station surrounding plant growth video information; and the analysis module is used for adjusting and optimizing base station equipment corresponding to each base station based on a preset base station equipment adjustment strategy according to the base station influence degree of main plants corresponding to each base station, and outputting a corresponding base station equipment adjustment scheme.

A tree and shrub identification tag device is provided. The device is comprised of a tag designed for efficient and adaptable plant identification while promoting environmental sustainability. The device comprises eco-friendly materials, including biodegradable and recyclable plastics, to ensure durability and weather resistance through UV protection additives and waterproof coatings. The tag body is available in various sizes and features a rubberized texture for enhanced grip. A notched slot at one end allows for secure attachment and easy removal, while perforations facilitate damage-free detachment from around a plant. The device may also be comprised of a color-coding system for categorizing plant types and includes a customizable surface for detailed labeling. Additionally, the tag may incorporate QR codes for accessing digital resources such as care guides and supplier information, enhancing overall plant management and user experience.

The application belongs to the technical field of vegetable breeding, and provides a SSR primer for identifying purity and authenticity of cucumber Jingyan Dongmei No.9 hybrid. The SSR primer provided by the application has polymorphism among parents, and all shows co-dominance, and shows clear, stable and parent complementary band type in hybrid offspring, and has strong specificity, and can be used to distinguish mixed maternal, paternal and other variety seeds; the application provides a convenient, accurate and efficient identification method for identifying purity and authenticity of Jingyan Dongmei No.9 variety, and can completely replace field planting identification to avoid resource waste, save land and manpower and material resources.

The application provides a rape seedling stage hetero-plant positioning method and a hetero-plant removing method, wherein the rape seedling stage hetero-plant positioning method comprises the following steps: obtaining an image containing a plurality of seedling stage plants in a plot; identifying all the seedling stage plants in the image to obtain plant pictures, and configuring index information for each plant picture; extracting features of each plant picture based on high-dimensional features of a preset dimension to obtain a feature vector; calculating the Euclidean distance between any two feature vectors to determine the outlier index of each feature vector; presetting an outlier index threshold to screen plant pictures corresponding to feature vectors whose outlier indexes exceed the preset outlier index threshold; and determining the position information of the screened plant pictures according to the index information to obtain the position information of the hetero-plant. The application is used to solve the problem that in the prior art, a large amount of variety and feature data is required in the database, and it is still difficult to achieve high-precision hetero-plant identification.

The application discloses a kind of molecular markers for Camellia genus Camellia reticulata group plant identification, which is composed of 3 Camellia reticulata specific DNA sequences.The application also provides a Camellia reticulata identification method based on the molecular markers, and the operation process is as follows: total DNA extraction is carried out on the sample to be tested, then high-throughput sequencing is carried out, the sequencing reads obtained are compared with the molecular markers, and if the sequencing reads can completely cover the molecular markers, it can be determined that the sample to be tested belongs to Camellia reticulata.

This invention relates to the field of plant identification technology and discloses a method for single-plant identification and stem-leaf separation based on 3D point cloud data processing technology. The method includes: acquiring multi-view 3D point cloud data of the target plant region to obtain raw point cloud data; preprocessing the raw point cloud data to obtain preprocessed point cloud data; determining the single-plant point cloud dataset based on the preprocessed point cloud data and a single-plant segmentation model; extracting features from the single-plant point cloud dataset to obtain the geometric, texture, and color features of each point cloud; determining the type of point cloud based on its geometric, texture, and color features; and obtaining the stem-leaf separation result based on the point cloud type. For plants with complex and narrow structures, this method accurately segments single plants and distinguishes stems and leaves through multiple features, improving the accuracy of separating stems and leaves from special-shaped crops.

The invention provides a method for efficiently removing abnormal plants in the rape seed production and cultivation process, and belongs to the technical field of agricultural seed production. The method comprises the steps that the shape and area parameters of a rape field are obtained, grid-shaped division is conducted in combination with the optimal plant spacing and the size of an intelligent mobile robot, grids are sowing blocks, and a rape seed is sown; when the rape seeds grow to a preset stage, an intelligent mobile robot carrying an image recognition and impurity removal execution module is moved to the front of a typical rape plant which is manually confirmed, an image recognition module recognizes an image and extracts features, and target image features are obtained; the robot is driven to move along the moving path, the image recognition module recognizes all rape plants in the field according to target image features, and when the rape plants are recognized as abnormal plants, the impurity removal execution module is started to remove the abnormal plants. According to the method, through accurate planning, standard plant calibration and automatic identification and impurity removal, the efficiency, precision and cost control of impurity plant removal are considered, and technical support is provided for intelligent and large-scale development of rape seed production.

The invention belongs to the technical field of plant identification, and relates to a ginseng appearance quality detection method fusing multi-scale feature extraction and knowledge distillation, which comprises the following steps: 1, data acquisition: uniformly and respectively shooting ginseng samples from multiple angles to obtain a ginseng sample data set; 2, data enhancement: dividing a ginseng sample data set into a training set, a verification set and a test set in proportion; 3, constructing a YOLOv11n network; according to the method, efficient operation can be realized on edge equipment, so that the method is particularly suitable for being deployed under field conditions with extremely high requirements on real-time analysis; the capability greatly improves the accuracy and reliability of crop monitoring and management, and makes an important contribution to the development of intelligent agriculture.

The application discloses a kind of urban green plant identification method, device, medium and equipment, it is related to image recognition technical field, including obtaining remote sensing image to be identified;Deep feature in remote sensing image is extracted, obtain multiscale spectral feature in deep feature, deep feature and multiscale spectral feature are fused, generate multispectral feature map;Wherein, the deep feature includes color feature and texture feature;Strip feature and multiscale shape feature in remote sensing image are extracted, and strip feature and multiscale shape feature are fused, generate multi-shape feature map;By corresponding weight of multispectral feature map and multi-shape feature map is weighted fusion, determine the feature map after fusion;The resolution of the feature map after fusion is restored to original image size, then the feature map of recovery resolution is classified, obtains the final urban green classification result.

The application discloses a plant carbon fixation amount measuring and calculating method based on a UAV oblique photography technology, and comprises the following steps: 1, collecting and scanning image data of a plant scene; 2, obtaining a vector model of the plant scene by adopting an oblique photography technology to vectorize the collected image data and derive a vector model; 3, screening and identifying different plants in the vector model; 4, identifying and screening different parts of the plant tissues; 5, calculating the volume of the different parts of the plant tissues; and 6, calculating the carbon content of the plant. The application is based on a UAV and an oblique photography technology, and is assisted by a ground shooting technology, so that accurate analysis of the carbon fixation amount of arbor greening resources can be realized, timely and fast data comparison can be provided, and the application can also be used as an important review method for calculating the carbon fixation amount in the field of greening carbon sink.

This invention discloses a multi-source remote sensing identification method for allergenic wormwood based on FSDAF spatiotemporal fusion. It integrates high-resolution remote sensing satellite imagery (optical and radar images) combined with UAV data and multi-source data from ground meteorology and vegetation monitoring. Utilizing advanced technologies such as FSDAF spatiotemporal fusion, it constructs an identification model for allergenic wormwood based on the spectral, texture, density, and geometric characteristics of the plant's growth period. This model identifies severely invaded areas of allergenic wormwood, achieving high-precision automatic identification and spatial distribution mapping of allergenic wormwood over large areas. Based on the identification model and growth status analysis model, it accurately analyzes the distribution area and severity of damage of allergenic wormwood within different administrative divisions, providing a basis for implementing targeted wormwood control measures.

The present application relates to a kind of portable plant community species composition identification device, it is related to research tool technical field, including draw-bar box type box, box is equipped with collapsible support rod, the end of support rod is hingedly equipped with standard measurement frame, for enclosing the plant to be measured, the plant to be measured includes shrub, herbaceous plant and arbor plant, standard measurement frame is equipped with sliding cross bar, cross bar is equipped with mobile plant identification measurement component, plant identification measurement component is used to identify the kind of shrub or herbaceous plant being enclosed and measure the diameter of shrub and obtain the rhizome image of shrub or herbaceous plant;The top of box is equipped with third camera for identifying the kind of arbor plant and range finder for measuring the height of arbor plant.The identification device is convenient to carry and move, can identify the kind of different plants, can conveniently measure the diameter at breast height of arbor plant and height, and the diameter of shrub or the number of herbaceous plant.

This invention discloses a plant care method, apparatus, device, and storage medium. The method includes: using an XR camera to acquire multiple frames of images of a target scene; for each frame, using a plant recognition model to determine several plant objects contained in the image, and using XR technology to determine the anchor point coordinates of each plant object; for each plant object, using a species recognition model to determine the target species, using a health detection model to determine the health status, and combining the target species and health status to determine care information; and through the XR camera's visual interface, based on the anchor point coordinates, responding to the user with the target species of the plant object, its health status, and care information. This solution provides users with rich and diverse plant care information in an intelligent, efficient, and accurate manner, and leverages XR technology to provide intuitive and convenient 3D interactive responses to relevant content, thereby effectively improving the user experience while ensuring efficiency and accuracy.

1. The name of the design product: plant identification plate (Chinese herbal medicine planting plate). 2. The use of the design product: for plant identification plate, display plant information. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective drawing.

The invention relates to a method for capturing and processing a plant by means of a plant identification device (18), the plant identification device (18) having at least one camera module (22) and an electronic control unit (26), a first image and at least one second image of at least one or more individual plants (14, 16) being captured by means of the camera module (22), according to which method the plant identification device (18) detects the plant (14, 16). Based on the first image of the at least one individual plant (14, 16) and the at least one second image, whether each individual plant (14, 16) is a crop (14) or a weed (16) is distinguished in the electronic control unit (22) for each individual plant (14, 16), the distinguishing being performed by the control unit (26), in particular by means of a neural network, the method comprises the following steps: a) capturing a first image and at least one second image of individual plants (14, 16) using a camera module (22), where the first image and the at least one second image each have a different frequency range / spectrum, b) comparing the at least one second image with the first image, and c) comparing the at least one second image with the first image. And transforming the at least one second image using defined transformation parameters, in particular a transformation matrix, such that the at least one second image has the same viewing angle as the first image, in particular by means of homography, c) checking the first image and the at least one transformed second image to determine whether the individual plant (14, 16) is a crop (14) or a weed (16).

This invention provides a model and method for identifying different varieties of celery based on the characteristic odor differences of an electronic nose, belonging to the field of plant identification technology. The method of this invention includes the following steps: (1) using an electronic nose to collect odor sensing information of celery samples, including sensors sensitive to aromatic components, nitrogen oxides, sulfides, hydrides, alkanes, alcohols, and aldehydes; (2) using the stable response value of the sensor as a variable, performing cluster analysis and principal component analysis in sequence to obtain the preliminary classification and grouping of celery samples; (3) using orthogonal partial least squares discriminant analysis to verify the correctness of the grouping, constructing an OPLS-DA qualitative identification model, and the score map showing the formation of three independent clustering regions, with complete separation between groups and no overlap; (4) projecting the response value into the model and determining its variety category based on the projection position. This invention achieves rapid, accurate, and non-destructive identification of celery, Chinese celery, and hybrid offspring by combining electronic nose technology with multivariate statistical analysis methods.

The invention relates to the technical field of plant identification, and discloses a single plant identification and stem-leaf separation method based on a 3D point cloud data processing technology, and the method comprises the steps: carrying out the multi-view 3D point cloud data collection of a target plant region, and obtaining original point cloud data; preprocessing the original point cloud data to obtain preprocessed point cloud data; determining a single plant point cloud data set according to the preprocessed point cloud data and a single plant segmentation model; performing feature extraction on the point cloud data set of the single plant to obtain geometric features, texture features and color features of each point cloud; and determining the type of the point cloud according to the geometric feature, the texture feature and the color feature of each point cloud, and obtaining a stem and leaf separation result based on the type of the point cloud. For a narrow plant with a complex structure, single plant segmentation is accurately realized, stems and leaves are distinguished through multiple features, and the accuracy of separation of stems and leaves of crops with special forms is improved.

1. The name of the design product: monitoring device (grassland invasive plant identification). 2. The use of the design product: for real-time monitoring of grassland invasive plants. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application relates to the technical field of plant recognition, in particular to a natural image processing and analyzing method for plant recognition. Through the synergistic effect of a multi-scale Retinex algorithm and learnable weight parameters, accurate compensation of dark area illumination is realized, noise interference is effectively inhibited while the texture details of a plant main body are reserved, and a step-by-step processing mechanism combining a non-local mean filter and a directional convolution kernel is used to extract high-frequency details and strengthen edge outlines, so that the technical contradiction between noise suppression and detail reservation under low-illumination conditions in traditional methods is solved; and based on a correction system of color card matching and color distribution migration, through HSV space segmentation and histogram similarity measurement, the color deviation problem caused by artificial light sources is overcome, so that the color features of the plant main body are in a natural state.

The invention relates to a computer-implemented method for the image-based identification and classification of at least one plant in an agricultural field. The invention also relates to a device for dispensing a liquid onto at least one part of an agricultural field on the basis of the identified and classified information about the plants, and to a computer-readable storage medium on which the computer-implemented method is encoded.

The application discloses a plant identification method, device and equipment and a storage medium. When a to-be-identified plant in a to-be-identified image is shielded, the application acquires image features corresponding to the shielded plant, then performs image processing on the to-be-identified image according to the image features, acquires shape features and texture features corresponding to a target plant in the processed image, and then identifies the target plant according to the shape features and the texture features. When the to-be-identified plant in the to-be-identified image is shielded, the application can effectively inhibit the influence of the shielded plant on the to-be-identified plant by performing image processing on the to-be-identified image according to the image features, and can automatically and effectively identify the plant when the plant is shielded by identifying the target plant in the processed image according to the shape features and the texture features, thereby improving the efficiency and accuracy of plant identification.

1. The name of the design product: plant recognition interaction graphical user interface for electronic device. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: interface change state figure 4. 5. The use of the graphical user interface: the graphical user interface is used to display on the electronic device, the electronic device is the existing design, and the graphical user interface is mainly the plant recognition interaction function. Among them, the front view is the home page of the plant recognition interaction; the interface change state figure 1 is the interface displayed after clicking the "camera" icon at the bottom in the front view; the interface change state figure 2 is the interface displayed after clicking the "take a photo" icon at the bottom in the interface change state figure 1; the interface change state figure 3 is the interface displayed after waiting for a period of time in the interface change state figure 2; the interface change state figure 4 is the interface automatically displayed after the device recognizes a normal state plant in the interface change state figure 3; the interface change state figure 5 to the interface change state figure 11 are the interfaces displayed in turn after sliding down in the interface change state figure 4; among them, the use state reference figure 1 to the use state reference figure 12 are in turn a use state figure of the front view to the interface change state figure 11.