Abstract: A framework for diagnosing and predicting a suitability of soil conditions to various agricultural operations is performed in a combined, multi-part approach for simulating relationships between predictive data and observable outcomes. The framework includes analyzing one or more factors relevant to field trafficability, workability, and suitability for agricultural operations due to the effects of freezing and thawing cycles, and developing artificial intelligence systems to learn relationships between datasets to produce improved indications of trafficability, workability, and forecasts of suitability windows for a particular user, user community, farm, farm group, field, or equipment. The framework also includes a real-time feedback mechanism by which a user can validate or correct these indications and forecasts. The framework may further be configured to override one or more of the soil state assessments to ensure that indicators and forecasts are consistent with the recently-provided feedback.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A multi-step iterative process for simulating complex agricultural situations where limited sets of data are available for such problems first predicts an outcome for each situation in a particular dataset, using initial assumptions of an applied primary model. The process then uses the errors across these situations to identify where opportunities exist among relevant predictive variables for the model to make changes to a response to such predictor variables to reduce the errors when averaged across all situations. The process then develops a correction model to identify adjustments based on combinations of the predictive variables, and applies the adjustments to the primary model to induce an altered outcome.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.