Clearag, Inc.

Iteris, Inc.

Dtn, LLC

John J Mewes

Dustin M Salentiny

Robert C Hale

Dustin C Balsley

Dane T Kuper

Leon F Osborne

Thomas N Blair

Alex A Kurzhanskiy

Brent L Shaw

Kristopher A Zarns

Leif Pedersen

Douglas K Rand

Jeffrey J Kuntz

Gregory M Ostermeier

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.

Customized land surface modeling for irrigation decision support for targeted transport of nitrogen and other nutrients to a crop root zone in a soil system

A below-ground agricultural biological performance modeling approach in precision agriculture combines customized field modeling with machine learning techniques for environmental matching of variables to describe a below-surface soil state, to understand and predict the performance of soil-active agricultural biological products such as bio-pesticides, bio-stimulants, plant growth regulators, and other biologically-derives soil adjuvants. The modeling approach characterizes the influence of environmental relationships on the performance of such soil-active agricultural biological products to develop a suite of predictive models to provide notifications, advisories, and recommendations for appropriate products for individual fields.

Modeling and prediction of below-ground performance of agricultural biological products in precision agriculture

Customized land surface modeling in a soil-crop system using satellite data to detect irrigation and precipitation events for decision support in 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.

Modeling of crop growth for desired moisture content of bovine feedstuff and determination of harvest windows for high-moisture corn using field-level diagnosis and forecasting of weather conditions and observations and user input of harvest condition states

Modeling of time-variant threshability due to interactions between a crop in a field and atmospheric and soil conditions for prediction of daily opportunity windows for harvest operations using field-level diagnosis and prediction of weather conditions and observations and recent input of harvest condition states

Prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions and crop characteristics including sampled moisture content

A modeling framework for estimating crop growth and development over the course of an entire growing season generates a continuing profile of crop development from any point prior to and during a growing season until a crop maturity date is reached. The modeling framework applies extended range weather forecasts and remotely-sensed imagery to improve crop growth and development estimation, validation and projection. Output from the profile of crop development profile generates a combination of data for use in auxiliary farm management applications.

Continual crop development profiling using dynamical extended range weather forecasting with routine remotely-sensed validation imagery

Modeling of crop growth for desired moisture content of targeted livestock feedstuff for determination of harvest windows using field-level diagnosis and forecasting of weather conditions and observations and user input of harvest condition states

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.

Modeling of crop growth for desired moisture content of bovine feedstuff and determination of harvest windows for corn earlage using field-level diagnosis and forecasting of weather conditions and field observations

Modeling of crop growth for desired moisture content of bovine feedstuff and determination of harvest windows for corn silage using field-level diagnosis and forecasting of weather conditions and field observations

Customized land surface modeling in a soil-crop system for irrigation decision support in precision agriculture

A framework for combining a weather risk analysis with appropriate operational rules includes a data initialization component, a rules processing component, and one or more weather risk analysis and assessment tools to evaluate a flight condition. The framework applies current, historical, predicted and forecasted weather data to the one or more operational rules governing a mission, a payload, a flight plan, a craft type, and a location of the mission for aircraft such as an unmanned aerial vehicle or remotely-piloted vehicle, and generates advisories based on the evaluation of flight conditions such as a mission compliance status, instructions for operation of unmanned aircraft, and management advisories. The flight condition advisories include either a 'fly' advisory or a 'no-fly' advisory, and the framework may also provide a mission prioritization and optimization system.

Flight condition evaluation and protection for unmanned aerial vehicles and remotely-piloted vehicles

Mission prioritization and work order arrangement for unmanned aerial vehicles and remotely-piloted vehicles

Detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point. Images are clipped to align with the seed point and a bounding box around the seed point, and a mask is built by extracting edges of the field from the images. The workflow floods an area around the seed point that has pixels of a similar color, using the mask as an initial boundary. The flooded area is compared to threshold parameter values, which are tuned to refine the identified boundary. Flooded areas in multiple images are combined, and a boundary is built based on the combined flooded set. Manual, interactive tuning of floodfill areas allows for a separate boundary detection and identification workflow or for refinement of the automatic boundary detection workflow.

Image-based field boundary detection and identification

Growing community of inventors

Mayville, ND, United States of America

John J Mewes