Abstract: A computer-implemented method for determining field boundaries and crop forecasts in each field is provided. The method includes deriving vegetation indices for each geo-spatial pixel of each image of multi-spectral imagery at a plurality of points in time, constructing minimum bounding boxes for each image according to the vegetation indices, and generating, based on a neural network analysis of each image and the minimum bounding boxes, a geo-spatial plot of crops including a predicted plot of future crop usage for an area including each field in the multi-spectral imagery.

Abstract: A computer-implemented method is provided for improving farm management by determining farm boundary delineations within a target geographic area based on crop recognition. The method includes evaluating the data using a classification algorithm to generate one or more line segments at an interface separating each pixel in at least one pair of adjacent pixels. It also includes connecting the one or more line segments to an adjacent line segment to form a boundary delineation. The method further includes generating a boundary delineation map including the boundary delineation as well as generating a farm management plan including the boundary delineation map and a recommended crop type. The farm management plan can be generated based on at least a type of vegetation present.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes measuring a frozen precipitation level of the frozen precipitation in a collection vessel. The method further includes heating the collection vessel to convert the frozen precipitation into melted precipitation. The method further includes measuring a melted precipitation level to determine an equivalent water accumulation of the frozen precipitation.

Abstract: Described herein is a method of controlling soil moisture, water accumulation and fertilizer distribution in land. Elevation location data for land areas are extracted from two or more pixels of a topographic image including topographic data. Each pixel represents a land area. Wetness indices for the land areas are determined from the elevation and location data, based on the slope between two or more defined areas of land and an upslope contributing area per unit contour length. At risk defined areas of land, e.g., those at risk of accumulating water are identified based on wetness indices. Water is transported from the at risk defined areas of land to another location. The transporting of water reduces the risk of accumulating water in the at risk defined areas of land and improves crop growth potential in those areas.

Abstract: A system for increasing filter economy includes a memory configured to receive measurements of contaminants in an internal and an external environment. A cost effectiveness module is configured to determine a cost of a corrosion rate increase if unfiltered external air intake is increased for cooling, to determine a cost of increased air pressure to filter external air, and to determine if the cost of filtering external air exceeds the cost of the corrosion rate increase. An air intake module is configured to increase an intake of unfiltered external air if it is determined that the cost of filtering external air exceeds the cost of the corrosion rate increase.

Abstract: Systems for predicting a filter lifetime. A filter monitoring system includes a filter effectiveness history based on contaminant sensor information associated with a filter received from one or more contaminant sensors. A lifetime module is configured to determine a rate of filter consumption based on the filter effectiveness history and to determine a remaining filter lifetime based on the determined rate of filter consumption.

Abstract: A computer-implemented method for determining field boundaries and crop forecasts in each field is provided. The method includes deriving vegetation indices for each geo-spatial pixel of each image of multi-spectral imagery at a plurality of points in time, constructing minimum bounding boxes for each image according to the vegetation indices, and generating, based on a neural network analysis of each image and the minimum bounding boxes, a geo-spatial plot of crops including a predicted plot of future crop usage for an area including each field in the multi-spectral imagery.

Abstract: A weighing station device, a computer-implemented method and a computer program product for estimating a weight of an animal. The weighing station device includes a stall for the animal, a camera in the stall that is activated to capture a plurality of digital images of the animal when a presence of the animal is detected at the stall. A processor obtains the plurality of digital images from the camera, constructs a three-dimensional digital image of the animal from the plurality of digital images, determines a dimension of the animal from the three-dimensional digital image, and estimates the weight of the animal from the determined dimension and a model relating the dimension of the animal to the weight of the animal.

Abstract: A computer-implemented method for determining farm boundary delineations within a target geographic area, comprising extracting data from pixels of a satellite image of the target geographic area, evaluating the data using a classification algorithm to generate one or more line segments between adjacent pixels, the one or more line segments being representative of a portion of a boundary delineation, connecting the one or more line segments to an adjacent line segment to form a boundary delineation defining at least one parcel of land within the target geographic area, and generating a boundary delineation map including the boundary delineation.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: Embodiments for controlling precipitation collection vessels to accurately and efficiently collect, measure, and aggregate precipitation accumulation data are disclosed. In one embodiment according to aspects of the present invention, a computer-implemented method includes collecting snow in a plurality of collection vessels by orienting an open end of each of the plurality of collection vessels perpendicularly to a direction of the wind based at least in part on a wind model. The computer-implemented method further includes measuring a snow level of the snow in each of the plurality of collection vessels to generate snow level data for each of the plurality of collection vessels. The computer-implemented method further includes aggregating the snow level data for each of the plurality of snow collection by assembling the snow accumulation data from each of the plurality of collection vessels.

Abstract: Described herein is a method of controlling soil moisture, water accumulation and fertilizer distribution in land. Elevation location data for land areas are extracted from two or more pixels of a topographic image including topographic data. Each pixel represents a land area. Wetness indices for the land areas are determined from the elevation and location data, based on the slope between two or more defined areas of land and an upslope contributing area per unit contour length. At risk defined areas of land, e.g., those at risk of accumulating water are identified based on wetness indices. Water is transported from the at risk defined areas of land to another location. The transporting of water reduces the risk of accumulating water in the at risk defined areas of land and improves crop growth potential in those areas.

Abstract: A computer-implemented method is provided for improving farm management by determining farm boundary delineations within a target geographic area based on crop recognition. The method includes evaluating the data using a classification algorithm to generate one or more line segments at an interface separating each pixel in at least one pair of adjacent pixels. It also includes connecting the one or more line segments to an adjacent line segment to form a boundary delineation. The method further includes generating a boundary delineation map including the boundary delineation as well as generating a farm management plan including the boundary delineation map and a recommended crop type. The farm management plan can be generated based on at least a type of vegetation present.

Abstract: Described herein is a method of controlling soil moisture, water accumulation and fertilizer distribution in land. Elevation location data for land areas are extracted from two or more pixels of a topographic image including topographic data. Each pixel represents a land area. Wetness indices for the land areas are determined from the elevation and location data, based on the slope between two or more defined areas of land and an upslope contributing area per unit contour length. At risk defined areas of land, e.g., those at risk of accumulating water are identified based on wetness indices. Water is transported from the at risk defined areas of land to another location. The transporting of water reduces the risk of accumulating water in the at risk defined areas of land and improves crop growth potential in those areas.

Abstract: A computer-implemented method for determining farm boundary delineations within a target geographic area, comprising extracting data from pixels of a satellite image of the target geographic area, evaluating the data using a classification algorithm to generate one or more line segments between adjacent pixels, the one or more line segments being representative of a portion of a boundary delineation, connecting the one or more line segments to an adjacent line segment to form a boundary delineation defining at least one parcel of land within the target geographic area, and generating a boundary delineation map including the boundary delineation.