97 results about "Plant identification" patented technology

The invention relates to the technical field of vegetation remote sensing recognition, in particular to a multispectral and visible light remote sensing image fusion segmentation method and system and a medium. The method comprises the following steps: constructing a double-branch model comprising a visible light branch encoder, a multispectral branch encoder, a multi-scale feature fusion module and a shared decoder; inputting the visible light data into a visible light branch encoder to obtain a spatial feature map; inputting the multispectral data into a multispectral branch encoder to obtain a spectral feature map; the multi-scale feature fusion module applies a channel-space joint attention weight to an input feature map, then performs channel splicing according to scales, compresses the feature map to an original channel number by adopting 1 * 1 convolution, and then applies a channel attention weight to obtain a multi-scale fusion feature sequence; and the shared decoder analyzes the multi-scale fusion feature sequence and outputs an invasive plant segmentation mask image of the target detection area, so that high-robustness invasive plant identification under a complex background is realized.

The invention provides an automatic plant identification method and system based on a neural network model, and relates to the technical field of intelligent agriculture, and the method comprises the steps: inputting a composite feature map into a compressed and optimized lightweight convolutional neural network model, and outputting a plant type identification result and a confidence value in real time; dynamically binding a plant type identification result with a geographic information system (GIS) coordinate, generating an endangered plant space distribution thermodynamic diagram and triggering a real-time alarm; images are automatically collected and marked based on newly-added plant sample positions marked in the spatial distribution thermodynamic diagram, and weight parameters of the lightweight convolutional neural network model are periodically updated. The plant identification and protection capability is comprehensively improved through the links of data acquisition diversification and standardization, image processing high efficiency and scientificity, feature extraction and identification accuracy, endangered plant protection and dynamic monitoring, model continuous optimization and adaptability and the like.

Described herein are techniques for generating contextually rich plant images. A number of data captures of raw plant data are generated via a sensing unit configured to navigate a growing facility. Metadata is generated and assigned to the raw plant data including at least one of: plant location, timestamp, plant identification, plant strain, facility identification, facility location, facility type, health risk factors, plant conditions, and human observations. Images generated by the sensing unit are analyzed and pixel annotations are generated in the images based on their relation to one or more plant well-being features. Data tags are generated and assigned the data captures based on an analysis of the data captures. The data tags are text phrases linking a particular data capture to a specific threat to plant well-being.

The invention provides an intelligent garden maintenance method and system based on big data, a medium and a program product, and relates to the technical field of garden maintenance. The method comprises the steps that plant image data and environment state information (including temperature, humidity and illumination intensity) in a garden are acquired, a plant identification model which is trained through deep learning in advance is utilized, plant images are analyzed, plant species and growth state information are acquired, and a system combines the environment state information and the plant information to identify the plant species and the growth state information. And making a personalized irrigation scheme for the target plant by referring to a preset plant irrigation demand strategy library, including irrigation time, irrigation liquid components and irrigation volume. And finally, the irrigation equipment is controlled to execute irrigation operation according to the determined parameters, and intelligent and accurate maintenance of the garden plants is achieved.

A farming machine configured for identifying incorrectly configured plant identification models in farming machines is disclosed. The farming machine includes a control system configured to monitor the performance of a plant identification model to determine if it is appropriately configured for detecting plants in the field. To do so, the control system accesses images of plants in the field and applies a first plant identification model to the images to identify a probability plants in the images are a first class of plants. The control system applies a verification model to determine if plants in the field are the second class of plants (rather than the first) based on the determined probability. The farming machine applies a second plant identification model to the images to identify a second class of plants, and treats plants in the field identified as the second class of plant.

The invention provides a plant classification labeling method. The method comprises the following steps: S1, acquiring an orthoimage of a target area through an unmanned aerial vehicle, and segmenting to generate plant pattern spots; s2, superposing the orthoimage and the base map of the electronic map, and establishing a composite coordinate system; s3, displaying the superposed map on the mobile terminal, and receiving a marking instruction of a user on the pattern spots; when the user cannot identify the plant type, calling a plant identification API to classify the plant image uploaded by the user, and associating the result to the corresponding plant pattern spot; s4, storing the labeled plant data to a database, and supporting statistics and management of the vegetation types, number and distribution of the target area through a geofence function; s5, a visual interface is provided, a user is allowed to check detailed information of the plants by clicking map marks, the detailed information comprises names, photos and statistical results, through the plant classification marking method, the types, the number and the distribution of the vegetation in the statistical area can be systematically analyzed, data are accurate, and presentation is visual.

The invention relates to an unmanned aerial vehicle wetland plant accurate identification system based on a multi-dimensional monitoring fusion technology, and relates to the field of computer vision and image processing, and the method comprises the steps: extracting contour information and color features in a region image; performing point cloud fitting on the regional point cloud set by using a plant contour to construct a plant three-dimensional model; performing color rendering through plant color distribution to generate a standard plant three-dimensional model; and performing multi-dimensional feature similarity comparison on the standard plant three-dimensional model and a plurality of predetermined three-dimensional models, and setting the plant type corresponding to the maximum similarity as an identification result. The method can solve the technical problems that a traditional method often depends on manual labeling or simple feature extraction, is easily affected by the environment, and is low in plant recognition efficiency and insufficient in recognition accuracy. Through the unmanned aerial vehicle platform, in combination with multi-source data of optical imaging and laser radar, accurate identification of plant species can be realized, and the accuracy and efficiency of wetland plant identification are significantly improved.

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 invention refers to a system, device, and method for identifying and analyzing the plants, pests, insects, and other elements of a crop to control the spraying of agrochemicals in real time, monitor and analyze planting conditions, and present data to the user for decision-making.The system comprises a device (7) on the spray boom of the agricultural vehicle that communicates with server (5) in the cloud that communicates with the user interface platform (8) and with the operator interface (10) in the cabin of the sprayer vehicle. The device (7) captures the images during operation, processes and decides the need for spraying, and controls the solenoid valves (3) of the spray nozzles, stores the images and information acquired in the field in memory, and sends them to the server (5) via the Wi-Fi connection.

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 an immature bitter orange-based original plant identification method based on an InDel molecular marker and a special primer combination, and belongs to the technical field of traditional Chinese medicinal material identification. In order to solve the problem of uncontrollable quality of medicinal materials caused by miscellaneous immature bitter orange base (lime, sweet orange and Yichang orange) in the prior art, two pairs of specific primers YCC-F / R (SEQ ID NO: 1-2) are screened out through genome re-sequencing and are used for specific identification of the Yichang orange (amplifying a single bande-1000bp); the invention relates to a specific primer for identifying three primary plants, in particular to a specific primer for identifying three primary plants, namely SC-F / R (SEQ ID NO: 3-4), which is used for distinguishing acid orange (single bande-1300 bp) and sweet orange (double bande-1200 bp / 1300 bp), and triple accurate identification of the three primary plants can be realized by combining PCR (Polymerase Chain Reaction) amplification and gel electrophoresis analysis. The method is simple and convenient to operate, low in cost, short in period and suitable for large-scale source tracing and quality control of medicinal materials.

The invention relates to the technical field of deep learning, and discloses a forest grass plant identification system based on deep learning, which comprises a data acquisition and management module used for confirming an acquired data set and carrying out primary processing on data, a data enhancement and generation module used for expanding the data volume, and an identification module used for identifying forest grass plants based on deep learning. The multi-modal feature fusion module is used for fusing plant appearance features and environmental data, the adaptive domain generalization module is used for cracking data distribution differences through data fusion, and the lightweight and real-time reasoning module is used for deploying lightweight model equipment to edge equipment. The federal learning collaborative optimization module is used for distributed training and model optimization, and the user feedback and continuous learning module is used for user feedback and online learning. Through the multi-modal feature fusion module and in combination with the adaptive domain generalization module, the system can effectively adapt to changes of different regions and seasons, and the performance reduction effect in a new scene is reduced.

The invention discloses an urban green plant identification method and device, a medium and equipment, and relates to the technical field of image identification, and the method comprises the steps: obtaining a to-be-identified remote sensing image; deep features in the remote sensing image are extracted, multi-scale spectral features in the deep features are obtained, the deep features and the multi-scale spectral features are fused, and a multi-spectral feature map is generated; wherein the deep features comprise color features and texture features; extracting strip features and multi-scale shape features in the remote sensing image, and fusing the strip features and the multi-scale shape features to generate a multi-shape feature map; performing weighted fusion on weights corresponding to the multi-spectral feature map and the multi-shape feature map to determine a fused feature map; and recovering the resolution of the fused feature map to the size of the original image, and classifying the feature map with the recovered resolution to obtain a final urban green land classification result.

The invention relates to a plant identification method and a related device. The plant identification method comprises the following steps: acquiring a plant image and a question text for identifying a plant in the plant image; inputting the plant image and the question text into a plant knowledge question and answer model, the plant knowledge question and answer model including a visual model configured to receive the plant image to extract image features of the plant image and a large language model configured to extract the plant image; the large language model is configured to receive image features and question texts to identify plants in the plant images, the large language model is pre-trained continuously by using a synthetic corpus generated based on a plant domain corpus on the basis of pre-training, and the plant knowledge question and answer model is trained by using multi-modal data; the multi-modal data comprises a plant image, a question for identifying a plant in the plant image and an answer to the question; and outputting an answer text which is provided by the plant knowledge question and answer model and is related to plant identification in the plant image.

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.

A system and method for automating the identification of botanicals and detection of adulterants based on High Performance Thin-Layer Chromatography (“HPTLC”) images. The system and method includes a first neural network that augments an existing HPTLC image dataset with synthetic data created using an adversarial machine learning model. For example, the synthetic data may be created using a generative adversarial network (GAN). The system and method further includes a second neural network that is trained on a combination of real data and synthetic data produced by the adversarial machine learning model. For example, the second neural network may be a deep convolutional neural network (CNN). Following training, the CNN performs the identification of botanicals and detection of adulteration through machine vision-based analysis of the output image data corresponding to phytochemical composition from HPTLC. The system may provide confidence-based probabilities and other numerical outputs related to the identify and adulteration determinations.

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.

The embodiment of the invention provides a plant recognition processing method and device.The plant recognition processing method comprises the steps that in the plant image processing process, recognition cue words are generated for plant images uploaded by a user terminal, and the plant images and the recognition cue words are input into a large language model for shape feature recognition; and according to the obtained shape features, performing graph conversion on the plant contour graph of the plant image to obtain a geometric graph, and finally generating graph knowledge data of the geometric graph and returning the graph knowledge data to the user terminal, thereby performing graph knowledge learning related to the plant shape on the basis of identifying the shape of the plant image.

To support species conservation and the grasping an invasion range of alien species by grasping animals and plants posted on an SNS and blogs.SOLUTION: A system includes means for grasping animals and plants posted on an SNS and blogs, means for storing the grasped information in a database, and means for retrieving habitat information of the animals and plants from the database.SELECTED DRAWING: Figure 1

To provide a system according to an embodiment, the system intending to propose an optimum management method depending on a species of a plant and a state of soil.SOLUTION: A system according to an embodiment includes: a plant identification part; a soil analysis part; and a proposal part. The plant identification part identifies a species of a plant. The soil analysis part includes specific methods for analyzing a condition or a nutritional state of soil on the basis of the species of the plant identified by the plant identification part. The proposal part proposes a management method of the plant on the basis of the information analyzed by the soil analysis part.SELECTED DRAWING: Figure 1

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.

A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

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.

A laser-based weed control module (1) and a device and a method for laser-based weed control, which permit targeted control of weed plants (17) on agricultural areas (15) with high effectiveness and simultaneously high efficiency. The weed control module (1) comprises an image detection unit for providing three-dimensional image data, a laser unit (4) for generating and orienting a laser beam (5) and a data processing and control unit (6), wherein the data processing and control unit (6) contains a trained artificial intelligence that detects weed plants (17), identifies survival essential plant part of the weed plants (17) and controls the weed plants (17) by targeted laser-irradiation the survival essential plant part of the weed plants (17) in an environmentally friendly and chemical-free manner. The generated laser beam (5) has a light wavelength (A) in the range from 955 nm to 995 nm.