272 results about "Precision agriculture" patented technology

Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and modeling module is provided which allows users to interactively and flexibly analyze and mine spatial data. The spatial data analysis and modeling module applies spatial data mining algorithms through a number of steps. The data loading and generation module obtains or generates spatial data and allows for basic partitioning. The inspection module provides basic statistical analysis. The preprocessing module smoothes and cleans the data and allows for basic manipulation of the data. The partitioning module provides for more advanced data partitioning. The prediction module applies regression and classification algorithms on the spatial data. The integration module enhances prediction methods by combining and integrating models. The recommendation module provides the user with site-specific recommendations as to how to optimize a recipe for a spatial environment such as a fertilizer recipe for an agricultural field.

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

The invention provides for a vertical takeoff and landing capable aerial vehicle with multiple rotors that is designed to carry agricultural sensors and telemetry allowing for real time control of agricultural equipment in accord with sensor data. The ability to carry a suite of agricultural sensors combined with multiple rotors will allow the craft to operate quickly in hovering and longitudinal flight over rows of farm fields and other vegetation and use an NDVI imager and other sensors to take data readings and real time imagery which will allow farmers and other personnel to determine vegetation type, need for chemical applications, plant fertilization, irrigation requirements, and other vegetation features including types of vegetation present. This will allow for precision agricultural, vegetation, and crop management and for farmers it will increase the efficiency of precision agriculture operations.

A pest and disease modeling framework for precision agriculture applies weather information, pest biological characteristics, and crop management data to anonymous crowd-sourced observations of pest presence for a reporting field. A risk assessment profile of pest occurrence for targeted fields in proximity to reporting fields is modeled to generate field-specific measures for pest management of pest infestation. The pest and disease modeling framework matches and filters weather and crop information in infested and pest-free fields based on the anonymous, crowd-sourced reporting of an existing pest presence, by evaluating similarities in pest-relevant data. Fields that are similar to infested fields have the highest risk of infestation, and the modeling framework provides output data in the form of a prediction of pest occurrence based on the risk assessment profile.

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

An embodiment of the invention discloses a hyper-spectral estimation model constructing method of total nitrogen content of rice leaves. The method comprises steps as follows: multiple experimental plots are selected, and multiple sampling points are selected in each experimental plot; canopy spectral measurement is performed at the critical growing stage of rice; multiple sampling spectrums are recorded at each sampling point, and an average value is taken as a spectral measurement value of the sampling point; a hyper-spectral image of each experimental plot is acquired by an airborne imaging spectrometer; multiple function leaves at different parts are collected at each sampling point, and the total nitrogen content of the rice leaves is measured; the hyper-spectral estimation model of the total nitrogen content of the rice leaves is constructed with the adoption of spectral indexes or a partial least-squares regression method. The embodiment of the invention further discloses a hyper-spectral estimation method of the total nitrogen content of the rice leaves. The total nitrogen content of the rice leaves is estimated according to the model constructed with the method. The scientific and technical basis can be provided for space inversion of the nitrogen content of regional-scale rice and efficient implementation of precision agriculture.

The invention discloses a rice leaf blast detection and classification method based on the multi-spectral image processing. A visible / near infrared multi-spectra camera is used for real-timely collecting monochromatic gray images of three waveband channels of green light wavebands, red light wavebands and near infrared wavebands, and then Matlab software is used for programming application software by an image processing method for image processing. The method comprises the removal of background, noises, interferences, and the like, and the recognition analysis of crop disease plague information, and the accurate and rapid processing of whether crops are ill and the position and the classification of disease plagues are realized. The disease recognition time of each picture is only a few seconds. The rice leaf blast detection and classification method based on the multi-spectral image processing is used for the rapid, accurate, stable, real-time and non-destructive crop leaf blast infection diagnosis and for accurately indicating the position of the disease plagues and the classification of infection grades, reducing the dosage due to the overall spraying, lowering the production cost, reducing pollution, providing data support for the variable spraying and improving the decision level of the accurate spraying. The method plays an active role in realizing the precision agriculture.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and / or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and / or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and / or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and / or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

Methods and software for selecting seed products or other agricultural inputs for planting within an associated land base include dividing the field into regions, providing an environmental profile for each of regions, determining a recommendation of a seed product to plant within each of the regions based on the environmental profile and performance of the genotype of each of the seed products in the environmental profile of the regions, and providing an output identifying the seed product to plant within each of the regions.

The invention discloses a method for modeling a rice and wheat leaf nitrogen content spectrum monitoring model and belongs to the field of nondestructive monitoring of crop growth information in precision agriculture. The method comprises the following steps of: fusing rice and wheat canopy leaf reflectance spectrum data which is acquired by a field high-spectrum radiometer with rice and wheat canopy leaf nitrogen content data; and establishing a canopy leaf nitrogen content spectrum monitoring model orienting different growth periods of rice and wheat based on narrow band and wide band combination. The optimal vegetation index orienting a rice and wheat jointing and booting stage and a heading and grain filling period is established by utilizing multi-point rice and wheat field test data in many years; and rice and wheat canopy leaf nitrogen content common characteristic wave band and bandwidth are developed. The model covers different species and different nitrogen levels of rice and wheat, utilizes a data validation model in an independent year, and is high in universality and high in accuracy.

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

The invention discloses a data analysis based agricultural guidance system. The data analysis based agricultural guidance system comprises a sensing module used for acquiring crop planting parameters, including, at least one of climate temperature, climate humidity, soil humidity, a soil pH value, a oxygen amount and a carbon dioxide amount; an image module used for acquiring crop image data; a climate data module used for obtaining climate data where the crops are within a preset time period; a guidance center used for receiving the data acquired by the sensing module, the image module and the climate data module, calculating generated indexes and a production predicted value corresponding to the crop parameters, image characteristics and the climate data after extracting the image characteristics of the image data, querying a preset database to obtain corresponding guidance information and pushing the guidance information to mobile devices of farmers. The data analysis based agricultural guidance system decreases a monitoring threshold in an agricultural product production process, reduces planting difficulty and production cost and is suitable for development of modern precision agriculture.

The invention discloses a hyperspectral image classification method based on image regular low-rank expression dimensionality reduction. The method includes the steps that a mean shift technology is used for conducting pre-segmentation on a hyperspectral image first, image regular low-rank coefficient expression is conduced on the hyperspectral image after pre-segmentation to obtain an image regular low-rank coefficient matrix, a characteristic value equation is constructed, a mapping matrix of the dimensionality reduction is studied, and original high dimensional data are transformed to low-dimensional space to be further classified. According to the hyperspectral image classification method, a hyperspectral image local manifold structure is excavated, the spatial distribution character of an original image is kept, effective dimensionality reduction space is studied, the classification accuracy of hyperspectral images is improved, computation complexity is lowered, the problems that the dimensionality of the hyperspectral image is too high so that the calculation amount can be large, and an existing method is low in classification accuracy are mainly solved, and the hyperspectral image classification method can be used for important fields such as precision agriculture, object identification and environment monitor.

A self-contained UAV sensor payload and on-board or off-board hardware and software for precision agriculture. The invention combines multiple cameras and navigation sensors to create a system for automatically collecting, geo-referencing, and stitching red, green, blue, and near infrared imagery and derived vegetation indices. The invention is able to produce all of these quantities in a single flight. The tight integration with integrated navigation sensors eliminates the need for integration with external sensors or avionics hardware and enables innovative solutions to image processing and analysis which greatly improve accuracy, processing speed, and reliability over alternative approaches.

The invention discloses a corn directional seeding method, a directional seed block manufacturing equipment and a device for seeding seed blocks, which aim to overcome the phenomenon that corn leavesare mutually interfered and shield sunlight, and enable the spreading direction of the leaves to be consistent and vertical to a ridge direction to improve planting density and yield. The method comprises the following steps: firstly manufacturing seed strips consistent to biological characteristics of corns, and directionally loading seeds onto carriers for directional seeding; and making germ surfaces of the seeds loaded onto the carriers parallel to the ridge direction, germs vertical to the ridge direction, tops of the seeds upward and the germ surfaces facing outsides of the seed blocks.The carrier seed block manufacturing equipment has the following structure: a directional module is arranged in a hole of a seed block template; the inside of the directional module is provided with aseed positioning slot hole and a seed pushing mandrel; a seed strip box and a feeding device are arranged above the seed block template; the height difference is controlled to complete seed positioning, material filling and demoulding. The device for seeding the seed blocks comprises a seed block passage, a seed feeding mechanism, a colter and a seed block charging mechanism. The method, the equipment and the device overcomes the defect that the leaves are disorderly arranged, and can greatly improve corn planting density to improve the yield and show the advancement of precision agriculture.

The invention discloses a hyperspectral plant leaf disease and insect pest diagnosis system belonging to the field of agriculture. A hyperspectral imager and a line light source are together fixedly connected with an electric control displacement platform, and move together by control of a displacement platform control device; in the process of moving, the line light source irradiates a calibration board and a to-be-measured plant leaf, the hyperspectral imager collects data and uploads to a computer in real time; through image analysis and processing by processing software, the data is inputted into a disease and insect pest triple diagnosis model, the plant disease and insect pest infection degree can be quantitatively obtained in real time, according to the analysis, decision on whether a to-be-measured plant needs to be sprayed with a pesticide can be made, and decision on pesticide dose and type and spray head form can be made. The hyperspectral plant leaf disease and insect pest diagnosis system is mainly used for the fast nondestructive diagnosis of plant diseases and insect pest infection degree, and can play a positive role to improve the decision-making level of precision pesticide application and implement precision agriculture.

An intelligent yield estimating system based on remote data transmission is composed of master control system, voltage-stabilized power supply, running speed sensor, impact sensor for measuring the impact of grains stream, humidity sensor for measuring the humidity of grains, harvesting bench sensor, GPS sensor, speed sensor for grains lifting unit, and radio transmission module.

Provided is a method for prediction of soil and / or plant condition. The present invention relates to a method and system for predicting soil and / or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest according to claim 1 and claim 8, respectively, particularly wherein the assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and / or to an agricultural device based on acquired measurement data, particularly remote sensing data,and a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and / or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure.

The invention belongs to the technical field of automatic measurement and control of agricultural mechanical equipment and relates to a fruit-tree precise positioning mobile vehicle-mounted device for an orchard, in particular to a fruit-tree positioning map constructing method, comprising a mobile platform, a GPS (global position system) positioning module, a laser detection module and a vulgar cruising module. According to the characteristics of the distribution of the fruit trees in the orchard, a wheeled mobile platform which can walk between the rows of fruit trees is adopted, and a laser distance measuring instrument, an inertial measurement device, the GPS positioning module, a radar speed measuring instrument and a measuring and controlling board card are combined together, thus the fruit trees are positioned and identified quickly and effectively in the low-speed cruising state, the information of the distribution of the fruit trees is obtained, the orchard positioning map is constructed in real time, the information of growth and distribution of the fruit trees are obtained accurately, and scientific technological means are provided for the accurate pesticide applying of the fruit trees, the scale operation of the orchard and the precision agriculture digital management.

A method and system for predicting soil and / or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest. The assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and / or to an agricultural device based on acquired measurement data, particularly remote sensing data, and wherein a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and / or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure.

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

The system is composed of information collection modules, transmission module of data wireless network, remote receiving and monitoring and controlling platform module. Through communication cable, collection modules are connected to the transmission module. Signal transmission is carried out in wireless mode between the transmission module and platform module. Collecting and processing information of soil moisture at each monitored point, the collection module transfers collected and processed information to the transmission module through cable. The transmission module in wireless mode transmits information of soil moisture to the platform module. The platform module receives, manages and issues information of soil moisture, and controls monitored points. The invention realizes unmanned collection of information of farmland automatically in real time without limitation of space, time and number of monitored points.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and / or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and / or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

The invention discloses a hyperspectral image space-spectral domain classification method based on mean value drifting and group sparse coding and mainly solves the problems that a hyperspectral image is low in classification accuracy and poor in robustness in the conventional method. The method is implemented by the steps of: inputting a hyperspectral image, and representing hyperspectral samples by feature vectors; randomly selecting marked samples in the hyperspectral samples, and carrying out over-segmentation on the hyperspectral image; grouping the hyperspectral samples according to the segmentation result of the hyperspectral image; carrying out sparse coding on the hyperspectral samples by utilizing the group sparse coding; constructing a sample set by utilizing the hyperspectral samples and sparse coding coefficients of the hyperspectral samples; and carrying out classification on the hyperspectral samples by utilizing a support vector machine and the constructed sample set. According to the hyperspectral image space-spectral domain classification method based on the mean value drifting and the group sparse coding, the sparse characteristics and space-domain contextual information of the hyperspectral image are adequately utilized, the relatively high classification accuracy can be obtained, and the method can be applied to the fields of precision agriculture, geological investigation, survey and military reconnaissance and the like.

A device may receive sensor data from a sensor device located on a particular farm. The device may identify an alert, associated with the particular farm, based on the sensor data and using a model. The model may be created based on imagery data and numeric data relating to a group of farms. The device may determine, using the model, a recommended course of action to address the alert, and provide, to a user device associated with the particular farm, the recommended course of action.

The invention discloses a hyperspectral image classification method based on semi-supervised dictionary learning, mainly solving the problem of high dimension of hyperspectral image and low classification precision in small sample status. The hyperspectral image classification method includes: expressing the pixel dot of the hyperspectral image via the spectral feature vector; selecting mark sample set, no-mark sample set and the test sample set; forming class label matrix with mark sample; forming Laplacian matrix without mark sample; using the alternating optimization strategy and the gradient descent method for solving the semi-supervised dictionary learning model; using the learned dictionary for coding the mark sample, no-mark sample and test sample; using the learned sparse code as the characteristic for classifying the hyperspectral image. The hyperspectral image classification method based on semi-supervised dictionary learning adopts the semi-supervised thought to obtain higher classification accuracy compared with the supervised learning method and is applied to the field of precision agriculture, vegetation investigation and military reconnaissance.

Provided is a wireless sensor network (WSN) node deployment method of precise agriculture. According to the method, a network is divided into cluster heads of two stages, the whole WSN network is composed of a plurality of first-stage fixed cluster heads (1), a plurality of second-stage cluster heads (2), a plurality of sensor nodes (3) and a Sink node. The first-stage fixed cluster heads are of a bus type topology structure, are distributed sequentially, and finally are connected with the Sink node. Each fixed cluster head is connected with the second-stage non-fixed cluster heads, each second-stage non-fixed cluster head is connected with the sensor nodes, and power of the first-stage fixed cluster heads and the Sink node is supplied by solar energy. The second-stage non-fixed cluster heads receive information collected by the sensor nodes in the coverage area of the second-stage non-fixed cluster heads, and first-time data fusion is carried out. The fixed cluster heads receive data after non-fixed cluster head fusion in the coverage area of the fixed cluster heads, second-time data fusion is carried out, and the data are uploaded to the Sink node one by one. The method has the advantages of being low in network energy consumption, high in reliability, little in delay property, strong in flexibility and the like, and is suitable for WSN node deployment of the precise agriculture.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and / or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and / or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyses.