46 results about "Precision agriculture" patented technology

The application discloses a rice nutrient precision monitoring and fertilization decision system based on multi-source data fusion, and particularly relates to the field of precision agriculture, and is used for solving the problems of nutrient monitoring deviation and inaccurate fertilization caused by spectral saturation during the rice vigorous growth period, and is achieved by the following steps: collecting red edge and near-infrared reflection from multiple angles, synchronously acquiring texture and structure images and fusing to generate an anti-saturation feature cube, outputting a vigorous growth period feature package by spatiotemporal segmentation at the phenological transition point, constructing a separable nitrogen trace spectrum by eliminating saturated response, outputting a prescription front graph by adjusting the balance coefficient under the calculation of energy concentration and water layer confluence indicators in a context constraint graph, and finally outputting an execution prescription graph by combining historical trajectories and micro-terrain to check the landing point, so that the vigorous growth period diagnosis precision is improved, over-fertilization or under-fertilization is avoided, and efficient nutrient management is realized.

This invention discloses an intelligent method for predicting crop water consumption in irrigation areas, belonging to the field of crop water consumption prediction technology. The method includes data acquisition, data processing, crop water consumption estimation, and crop water consumption prediction model construction. Data acquisition involves obtaining meteorological data, soil data, vegetation index, and crop data for the irrigation area. Interpolation and resampling methods are used to unify the spatial resolution of all data and align them temporally. The SEBAL model is used to estimate daily evapotranspiration, i.e., crop water consumption. A Transformer model is used to model crop water consumption, meteorological data, soil data, and crop growth information. Through these steps, this invention can achieve high-precision and rapid prediction of the future water consumption of different crops by inputting planting structure, planting time, soil data, and meteorological forecast data, providing strong technical support for water conservation and precision agricultural irrigation in irrigation areas.

The present disclosure provides a system and method for automated monitoring, quantification, and control of plant health from micro and macro scales using optical sensing and imaging modalities, environmental sensing, data analysis, image processing, and machine learning. Intelligent feedback control of plant health employed in the system reacts in real-time to changes in plant health to enable precision agriculture applications like intelligent irrigation or determination of optimal fertigation parameters. In turn, the system results in improved agricultural outcomes such as increased fruit production or unique flavor profiles. The system further provides a means for automated and non-invasive testing of novel treatments for disease or adverse conditions, or to improve crop yields.

In precision agriculture, high-precision navigation, attitude determination, and obstacle detection methods are used to conserve resources and achieve better results. The collected machine position and attitude data, as well as obstacle location data, can be effectively utilized to synthesize control algorithms for autonomous agricultural machinery. These algorithms are applied to coverage path planning, route planning, motion stabilization along a specified path, obstacle avoidance, and ensuring guaranteed behavior.

The application discloses a crop photosynthetic instrument measurement positioning method and auxiliary system based on multi-modal perception, and relates to the field of precision agriculture and intelligent sensing technology; through multi-modal perception and an intelligent decision system, the application realizes full-process automation from leaf positioning, environment adaptation to data acquisition; the system adopts a phenology-visual feature fusion positioning algorithm combined with a deep vision model, can accurately identify the three-dimensional coordinates and orientation angle of target functional leaves, dynamically adjusts the leaf chamber posture through a microenvironment-posture adaptation planning algorithm, eliminates environmental interference, and further ensures the stability of the measurement conditions through a bionic compliant mechanical arm and an airtightness monitoring module, and finally analyzes the photosynthetic rate variation coefficient through a sliding window, thereby significantly improving the data reliability.

This invention discloses an intelligent monitoring and early warning method and system for field diseases based on multimodal data, belonging to the field of agricultural information monitoring technology. The method divides the target field into several sub-regions using a grid, and deploys environmental sensing sensors and image acquisition devices in each sub-region. It establishes a data sampling time period and normalizes the multimodal data for each sub-region. The method calculates the environmental induction intensity value and crop characteristic deviation value, and calculates the multimodal comprehensive disease risk score. It calculates the risk evolution direction and direction sign value at adjacent time points, and calculates the induced consistency value of adjacent sub-regions. It sets a comprehensive risk threshold and an induced consistency threshold, and issues real-time early warnings for sub-regions exceeding the thresholds. This achieves closed-loop management of monitoring, analysis, and decision-making, improving the accuracy and timeliness of field disease monitoring, reducing disease spread losses, and supporting precision agricultural management and scientific prevention and control.

The invention relates to a crop phenotype information generation system for unmanned aerial vehicle multispectral remote sensing images. The method comprises the following steps: acquiring remote sensing images by using an unmanned aerial vehicle with a multispectral sensor and a visible light sensor; constructing and using a user-defined vegetation index calculation module, and extracting characteristic variables with biological significance from the data; performing preliminary screening on all vegetation indexes; carrying out model training, and carrying out system evaluation on a training result by adopting multiple performance evaluation indexes; loading the trained model to carry out field prediction; predicting and generating a two-dimensional prediction image consistent with the original image in size; the topdressing amount is calculated, and a fertilization prescription map suitable for guiding agricultural machinery operation is generated in combination with the unmanned aerial vehicle remote sensing image and the grid width input by the user. According to the method, full-chain visualization from remote sensing data preprocessing to prediction result output, crop parameter inversion estimation and farmland management prescription map automatic generation functions are realized, and an intuitive decision support tool is provided for precision agricultural practice.

The invention relates to the technical field of precision agriculture, in particular to a scientific fertilization method based on detection of soil conditions and microbial action, which comprises the following steps: collecting soil impedance and microbial indexes, correcting microbial distribution by using impedance depth, and executing environmental inhibition attenuation on nitrogen fixation and phosphorus solubilization rate by combining a carbon source and acid-base buffer capacity. And quantifying biological available nutrient supply, and generating a fertilizer discharge signal based on a crop demand gap and mechanical parameters. According to the method, the vertical cone index and the microbial gene abundance are collected, physical impedance data are introduced to correct the microbial spatial distribution weight, the metabolic activation model is constructed in combination with active organic carbon and the acid-base buffer capacity, and the actual nutrient conversion efficiency and the effective fertilizer supply total amount of nitrogen-fixing bacteria and phosphate solubilizing bacteria are quantified; biological fertilizer supply capacity is brought into a nutrient income and expenditure balance system, a discrete fertilizer discharge control instruction is generated according to actual gaps of crops and mechanical operation parameters, and the utilization rate of chemical fertilizer and agricultural ecological benefits are improved.

The application discloses an intelligent variable pesticide spraying and fertilization control system and a regulating method, belongs to the field of cross between land-air flight equipment and agricultural plant protection, and solves the problems that the existing system cannot adapt to double-mode operation, has low regulating precision, and has poor collaboration, etc., as a core component of a land-air flight motorcycle. The system comprises seven modules of sensing, control, execution, etc. The sensing module collects multi-dimensional parameters, the control module adopts a fuzzy PID algorithm, is linked with a power system and a navigation system, realizes automatic switching of land-air double modes and dynamic regulation of parameters, the execution module completes precise pesticide spraying and fertilization, and has functions of closed-loop feedback, fault diagnosis and the like. The regulating method comprises steps of initialization, parameter presetting, real-time acquisition, operation regulation and the like. The application improves operation precision and efficiency, reduces waste of agricultural materials and environmental pollution, is convenient to operate, is suitable for various plant protection scenes, can meet operation requirements of different crops, and promotes the development of precision agriculture.

This application discloses a multi-mode fusion method and system for calculating canopy height in fields and orchards, belonging to the field of agricultural remote sensing and precision agriculture monitoring technology. It utilizes an unmanned aerial vehicle (UAV) platform equipped with a lidar sensor, a multispectral sensor, and a pose acquisition module to simultaneously collect lidar point cloud data, multispectral image data, and timestamped pose data. Based on the pose data, spatial registration and stitching are performed on the point cloud data to generate a 3D point cloud model of the crop; geometric correction and stitching are performed on the multispectral image data to generate a multispectral orthophoto. The two are unified to the same geographic coordinate system to establish a spatial mapping relationship; spectral indices are calculated based on the multispectral orthophoto to identify land cover categories and regions; corresponding subsets of the point cloud are extracted based on the spatial mapping relationship to generate a digital surface model and a digital ground model; and canopy height is calculated by the difference. This application integrates multi-source data to achieve automated and high-precision calculation of canopy height for crops such as corn and fruit trees, and has strong applicability.

The application discloses a soil humidity downscaling method based on dynamic and static factors, which comprises the following steps: DEM data, GPM satellite precipitation product, and original satellite soil humidity data preprocessing; soil humidity multi-branch feature fusion based on dynamic and static factors; Swin Transformer feature reconstruction and boundary constraint; through double-scale DEM feature extraction, precipitation dynamic feature construction, and multi-branch Swin Transformer architecture, the physical rationality of the downscaling result is ensured, and the high-precision reconstruction problem of soil humidity spatial heterogeneity in complex terrain areas is solved; combined with dynamic and static factors, automatic high-resolution soil humidity generation is realized, which is beneficial to the direct calling of the soil humidity downscaling method based on deep learning, fast obtaining of fine and personalized soil moisture data, serving as the input of hydrological models and drought monitoring, and further promoting the in-depth development of precision agriculture and water resource management.

The utility model relates to a kind of seed-fertilizer same application hole drillers of air-suction seed-dropping-mechanical fertilizer-dropping, belong to the technical field of agricultural seeding machinery.It is composed of baffle 1, seed cover, seed guide disc, negative pressure seed suction disc, bearing end cover 1, bearing 1 with sealing ring in both ends, negative pressure air inlet shaft, layered double-cavity duckbill, carousel, guide slot, compression spring, fertilizer spoon, bearing 2 with sealing ring in both ends, bearing end cover 2, fertilizer cover, baffle 2.Through the mode of air-suction seed-dropping-mechanical fertilizer-dropping, seed-fertilizer same application is realized, the seeding accuracy is improved, the damage of mechanical seed taking to seed is avoided, and the purpose of precision seed-fertilizer same application can be realized, the design can effectively improve crop emergence rate, guarantee seedling growth quality, and provide reliable technical support for precision agricultural seeding.

The present application relates to the field of precision agriculture technology, in particular to a kind of agricultural machinery operation track area calculation method based on deep learning, it includes the following steps: using the monitoring equipment of high-precision GNSS module and sensor are carried, data is uploaded to cloud server for storage by wireless communication module, data is denoising and coordinate conversion processing, construct the deep learning model based on MaskRCNN, train model to be suitable for agricultural machinery track segmentation task, grid image is predicted, using DBSCAN clustering algorithm is clustered to track point set, the area of each field is calculated by contour extraction, topological relationship check and optimization processing using vector space analysis technique, through the way of precision verification compared with reference data, obtain the accurate measurement result of agricultural machinery operation track area.The present application solves the problems of weak adaptability in complex scene, insufficient anti-noise interference ability and dependence on track point density in the existing agricultural machinery operation area measurement technology.

The present application belongs to the technical field of crop disease monitoring, and particularly relates to a hyperspectral wheat scab severity monitoring method based on UFD and Triplet Attention-1D-CNN, comprising the following steps: S101. Adopting unwinding Fourier decomposition (UFD) to perform spectral enhancement processing on wheat canopy hyperspectral reflectance data; S102. Constructing an oversampling strategy based on class proportion IR regulation to perform balanced processing on the spectral samples; S103. Training a one-dimensional convolutional neural network model based on the balanced spectral samples to realize wheat scab severity grading identification. A complete technical scheme from data preprocessing, sample balancing to intelligent identification is formed, rapid, accurate and automatic grading monitoring of wheat scab severity is realized, and effective technical support is provided for precision agriculture decision-making.

The application provides a soil salt content dynamic inversion method and device, electronic equipment and storage medium, in the method, the direct and indirect causal relationship between the soil salt content, the environment drought and the crop growth is quantified by introducing a structural equation (SEM) model, which overcomes the limitation that the traditional method only pays attention to the correlation and cannot analyze the coupling effect, provides theoretical support for inversion, filters the input features of the CNN model based on the causal relationship between variables analyzed by the SEM model, realizes high-precision inversion of deep fusion of mechanism and data, significantly improves the soil salt content inversion precision, combines multi-temporal remote sensing observation data and spatial interpolation technology, realizes the leap from static evaluation to dynamic monitoring, and the application can accurately capture the spatiotemporal migration trajectory of the soil salt content and the dynamic response of the crop growth, and provides a reliable technical scheme for targeted regulation and scientific intervention of precision agriculture.

This invention provides a soil carbonate ion sensor based on CuO-Cu2O heterojunction nanowires, its preparation method, and a real-time monitoring device. It is used for in-situ, real-time, and continuous monitoring of free carbonate ions in soil. This invention employs an in-situ oxidation and topological transformation strategy to synthesize CuO-Cu2O nanowire heterojunctions with excellent chemical structural stability, broad-spectrum antibacterial activity, and alkali resistance on a copper foam substrate, serving as the sensor's sensitive material. The prepared CuO-Cu2O material is used as the working electrode and integrated with a counter electrode to construct an electrochemical sensing unit. This unit is further integrated with an ESP32 main control module and Internet of Things (IoT) technology to create a portable device, enabling real-time, continuous monitoring and wireless data transmission of free carbonate ion concentration in soil. The sensor fabricated in this invention exhibits high sensitivity (264.98 μA·mM). −1 cm −2 This invention features a low detection limit (15 ppb), a wide linear detection range (0.01–1600 ppm), rapid response (0.6 seconds), excellent selectivity and long-term stability (30 days), and can operate stably in a wide temperature range (0–40°C) and pH range (7–11) and in complex soil media. The invention has a simple manufacturing process and excellent performance, providing a new strategy for accurate and real-time monitoring of soil salinization, and has broad application prospects in precision agriculture and environmental monitoring.

The present application relates to the field of wisdom agriculture and digital twin technology, and discloses a three-dimensional point cloud plant segmentation method which is universal in the whole growth period of soybeans. By adopting dynamic multi-level sampling, the sampling rate can be adaptively adjusted according to the spatial sparsity and local complexity of the plant. While greatly reducing the amount of point cloud data and improving the processing efficiency, the key geometric structure features are completely retained. The multi-scale feature extraction and geometric perception attention mechanism can strengthen the feature expression of the regions with significant geometric changes of leaf overlap and stem-leaf junction. The segmentation accuracy and robustness are significantly improved. The encoder-decoder structure combined with the feature pyramid realizes efficient fusion of multi-scale features. Not only excellent performance is achieved in the whole period segmentation of soybeans, but also the method can be directly migrated to the seedling stage scenes of crops such as corn and tomato, and has strong generalization ability. It provides stable and reliable, and universal three-dimensional vision core technology support for soybean phenotype analysis, intelligent breeding, growth monitoring and precision agriculture.

The application relates to the field of agricultural information technology and discloses a VR glasses fruit maturity recognition system and method based on big data. The system comprises a multi-modal sensor array integrated in a VR glasses main body, which collects multi-dimensional physical data such as fruit internal chemistry, surface temperature and hardness; a data processing unit performs pretreatment and feature extraction; a big data analysis module adopts a weighted cosine similarity algorithm to match and analyze the processed data and a dynamically updated feature database to determine the maturity grade; a feedback optimization module continuously optimizes the database and model according to user interaction feedback; a visual presentation module superimposes the recognition result on a real fruit image in the form of a three-dimensional color atlas; and the application significantly improves the accuracy, robustness and user experience of fruit maturity recognition in a complex environment through multi-source information fusion and an online learning mechanism and is suitable for a precision agriculture scene.

The present invention relates to the technical field of precision agriculture. The invention relates to a system and a method for accurate application of a liquid to a target object in an agricultural field.

An AI-supported, intelligent and modular system for monitoring plant health for precision agriculture, consisting of: a deployment device configured for field surveillance, the deployment device being selected either as an airborne drone platform for aerial surveillance or as a ground-based rover platform for ground-based surveillance, depending on the field size and terrain; A detachable sensor module attached to the deployment device for acquiring field monitoring data, wherein the detachable transmitting module comprises: an embedded edge computing unit; a red-green-blue (RGB) camera; a near-infrared (NIR) camera; a temperature sensor; a position sensor; calibration reference targets stored on the edge computing unit; and a wireless communication interface, wherein the embedded edge computing unit is configured to: synchronize data acquisition from the RGB camera, the NIR camera, and the temperature sensor; acquire high-resolution images with the RGB camera and the NIR camera; and record temperature data, humidity data, and metadata including timestamps, elevation, orientation, and coordinates; an external storage medium that is connected via the wireless communication interface to the embedded edge computing unit of the detachable sensor module and receives all data captured by multiple sensors and the camera in real time from the embedded edge computing unit; and A desktop application runs on an external computer device with a processor, and this external computer device is connected to an external storage medium. The data stored on the external storage medium is shared with the desktop application.The desktop application includes a computing module that performs the following functions: retrieving the captured data from the external storage medium; preprocessing the captured data through noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), from the preprocessed data; applying artificial intelligence models to identify plant stress states based on the vegetation indices; segmenting an agricultural field into multiple health zones based on the identified plant stress states; assigning a severity level to each health zone; and generating recommendations for each health zone based on the severity level.

This invention belongs to the field of flexible optoelectronic devices and precision agricultural monitoring technology, specifically relating to a dual-confined control semiconductor fiber, its preparation method, and its application in plant microclimate monitoring. Through a dual innovative architecture of "spatial confinement crystallization of the internal fiber core" and "synergistic confinement through mesoporous-microphase separation in the external cladding," this invention achieves integrated functionality of high signal-to-noise ratio photoelectric detection and reversible humidity response, providing a novel flexible fabric platform for non-contact, distributed, real-time monitoring of plant transpiration dynamics.

The subject of the invention is a method for developing a computer tool that utilises learning in a feedforward neural network FNN for the remote diagnosis of chemical element content in plants. The method includes collecting a training dataset, consisting of hyperspectral data from cultivated fields and measurement data on element content in plant leaves. The hyperspectral data is normalised and converted into input vectors, after which the dataset is divided into a training set and a test set. The training of the FNN involves an input layer with 64 neurons, a hidden layer with 32 neurons, and an output layer with a single neuron performing a regression function. The network is trained to match the predicted element content values to actual measurement data. The trained network is validated using the test dataset to ensure diagnostic accuracy. The method is applicable in precision agriculture for analysing the chemical element content in plants. The invention also concerns a computer-implemented method for the remote diagnosis of chemical element content in plants using neural network learning.

A navigation path generation method, system, electronic device, and readable storage medium are disclosed, relating to the field of navigation. This invention alleviates problems in existing navigation path generation technologies, such as reduced positioning accuracy under crop canopy occlusion and significant influence from lighting conditions. The navigation path generation method includes: a data acquisition stage: acquiring 3D point cloud data using a lidar sensor; a planar projection stage: vertically projecting the 3D point cloud data to obtain a 2D projected point cloud; an inner crop row extraction stage: segmenting the 2D projected point cloud to obtain left-side and right-side crop fitting parameters; and a navigation path generation stage: performing a three-level smoothing process based on the left-side and right-side crop fitting parameters to obtain a smoothed navigation path, thus completing the generation of the navigation path. This invention is applicable to fields such as agricultural robots, autonomous navigation, lidar, point cloud processing, and precision agriculture.

The application discloses a high-throughput plant phenotype data analysis platform based on artificial intelligence technology, which comprises an image acquisition unit, a deep feature extraction unit, a phenotype feature extraction unit, a quality prediction unit and a comprehensive evaluation unit; deep features of plant images are extracted through a multi-scale convolutional neural network, adaptive multi-scale feature fusion is carried out through an attention mechanism, plant physiological and biochemical indexes are predicted based on a deep regression network, and a comprehensive phenotype evaluation index is calculated by using an entropy weight method-hierarchical analysis method combined weighting method; the application realizes rapid, accurate, non-destructive and systematic analysis of plant phenotypes, and provides an efficient and intelligent technical tool for plant breeding, quality evaluation and precision agriculture.

This invention discloses a nutrient-degrading enzyme, engineered microbial agents, and their application in the nutrient regulation of polymeric slow-release fertilizers, belonging to the field of enzyme engineering technology. This invention utilizes a specific polymeric slow-release fertilizer-degrading enzyme modified by artificial intelligence, which not only retains the degradation activity of the source strain enzyme but also achieves efficient heterologous expression while promoting a dual improvement in stability and catalytic efficiency. The constructed composite engineered strain exhibits no antagonistic effect, enabling low-cost, large-scale production of the degrading enzyme. The composite engineered strain can achieve precise regulation of nutrient release from polymeric slow-release fertilizers. By adjusting the ratio of engineered strains producing peptidases and amidases, precise supply of nutrients to different stages throughout the growth cycle of different crops can be achieved, resulting in complete degradation of polymeric slow-release fertilizers, significantly improving crop yield and nutrient utilization, and showing significant application prospects in the development of precision agriculture.

The application provides a kind of open field crop growth monitoring method and device, applied to precision agriculture technical field.The method comprises: collecting the multi-source sensing data of open field crop by inspection robot, the multi-source sensing data includes depth image data, position data and inertial measurement unit (IMU) data;Time synchronization processing is carried out on the multi-source sensing data using timestamp interpolation calibration method, space alignment processing is carried out on the depth image data and the position data with IMU coordinate system as reference, and dynamic error of multi-source sensor is corrected based on extended Kalman filtering algorithm to obtain space-time alignment state vector;The space-time alignment state vector is densified to generate a global three-dimensional map covering the entire open field crop, containing the three-dimensional morphology and spatial position of the open field crop;The global three-dimensional map is segmented semantically, the centroid position of each crop is identified, and the growth parameters of each crop are determined.

The application discloses a harvester remote intelligent driving system based on real-time communication and relates to the technical field of precision agriculture and intelligent equipment, comprising a perception network module, which is arranged in a farmland and collects multi-dimensional perception data streams; a data fusion module, which is used for performing space-time synchronous fusion processing on the perception data streams and generating a real-time environment digital twin model of the farmland; a cooperative planning module, which transmits the model to a remote control center through a low-delay communication link, generates optimal paths and obstacle avoidance decisions of multiple harvesters based on the model, and outputs a cooperative operation scheduling instruction set; an instruction distribution module, which distributes the instruction set to each harvester through a high-reliability link; and a vehicle-mounted execution module, which analyzes and executes the instructions to drive operation. The system realizes real-time panoramic digitization of the farmland environment and remote centralized cooperative control, and improves the automation level, cooperative efficiency and operation safety of large-scale harvesting operation.

This invention belongs to the field of soil improvement and precision agriculture technology, specifically involving a method for precise application of soil conditioners based on soil physicochemical properties, including the following steps: S1, grid-based sampling of the target field, measuring multiple physicochemical properties of soil samples at each sampling point, wherein the multiple physicochemical properties include at least soil pH, soil organic matter content, and soil cation exchange capacity (CEC); significantly improved accuracy: through grid-based sampling and spatial analysis, the degree and type of soil obstacles at different locations within the field are accurately identified, achieving "tailored measures based on soil conditions and application as needed"; good comprehensive improvement effect: breaking through the limitations of single-indicator improvement, multiple key physicochemical properties such as pH, organic matter content, and CEC are incorporated into a unified decision-making framework, and the optimal comprehensive application amount is given through a multi-indicator collaborative decision-making method, achieving a comprehensive improvement in soil quality.

The application provides a multispectral plant physiology-environment coupling monitoring wearable sensor and method, and belongs to the technical field of agricultural information sensing and plant phenotype monitoring. The multispectral plant physiology-environment coupling monitoring wearable sensor comprises an upper clamping arm and a lower clamping arm. The upper clamping arm comprises an upper base, an excitation light source arranged on the upper base and facing the upper epidermis of a leaf, two first multispectral detectors symmetrically arranged on both sides of the excitation light source, and an upper light guide layer covering the upper epidermis of the leaf. The excitation light source is located at the geometric center of the upper base. The lower clamping arm comprises a lower base, a second multispectral detector arranged on the lower base, and a lower light guide layer covering the lower epidermis of the leaf. The second multispectral detector is located on the optical axis of the excitation light source. The three multispectral detectors are arranged to obtain the reflection, transmission and absorption spectra of the leaf, and the physiological index is inferred by coupling with environmental factors, so that the plant health state can be monitored in real time in the scenes of precision agriculture, ecological monitoring and the like.