Abstract: A computer-implemented method for managing crop harvesting activities is implemented by a harvest advisor computing device in communication with a memory. The method includes receiving an initial date of a crop within a field, receiving an initial moisture value associated with the crop and the initial date, and receiving a target harvest moisture value associated with the crop. The method also includes receiving field condition data associated with the field. The method further includes computing, by the harvest advisor, a target harvest date for the crop based at least in part on the initial date, the initial moisture value, the field condition data, and the target harvest moisture value, and displaying the target harvest date for the crop to the grower for harvest planning. The target harvest date indicates a date at which the crop will have a present moisture value approximately equal to the target harvest moisture value.

Abstract: In an embodiment, the disclosed technologies include an apparatus for storing data and communicating data between a vehicle or an agricultural implement and a computing device. Embodiments include a non-conductive housing, an antenna coupled to the non-conductive housing, an integrated circuit coupled to the antenna, a thermally and electrically conductive housing coupled to the integrated circuit, at least one ground clip coupled to the thermally and electrically conductive housing, at least one other integrated circuit coupled to the at least one ground clip, a memory coupled to the other integrated circuit and arranged to at least temporarily store digital communications between a vehicle or an agricultural implement and the computing device, and a connector communicatively coupled to the memory and arranged to mate with a connector of the vehicle or the agricultural implement.

Abstract: A computer-implemented method for determining agricultural revenue is provided. The method uses an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, calculating at least one yield projection for the field region based on the field definition data and the subset of the plurality of input data, and providing the at least one yield projection to a user device.

Abstract: A system for generating digital models of nitrogen availability based on field data, weather forecast data, and models of water flow, temperature, and crop uptake of nitrogen and water is provided. In an embodiment, field data and forecast data are received by an agricultural intelligence computing system. Based on the received data, the agricultural intelligence computing system models changes in temperature of different soil layers, moisture content of different soil layers, and loss of nitrogen and water to the soil through crop uptake, leaching, denitrification, volatilization, and evapotranspiration. The agricultural intelligence computing system creates a digital model of nitrogen availability based on the temperature, moisture content, and loss models.

Abstract: A method for predicting field specific crop yield recommendations is disclosed. A computer system receives data records, including remotely sensed spectral property of plant records and soil moisture records. The system aggregates the records to create geo-specific time series over a specified time. The system selects representative features from the geo-specific time series and creates, for each specific geographic area, a covariate matrix in computer memory comprising the representative features. The system assigns a probability value to a component group in a set of parameter component groups, where each component group includes one or more regression coefficients and an error term calculated from probability distributions.

Abstract: Display of graphical maps of agricultural fields, coded with color or other indicators of values of data pertaining to agronomy at high resolution, and updated on a daily basis or on demand by recalculating agronomy models with the high-resolution data, is disclosed. Map displays may include multiple layers that relate to different agronomy metrics, and GUI widgets that are programmed to receive selection of values indicating different field properties or layers to display.

Abstract: In an approach, a method for fusing point data with areal averages is performed by a computing system. The fusion procedure is coherent, in the sense that the computing system takes into account what the areal averages represent with respect to the point data. The overarching goal is to fit a model that takes into account the information derived from both data sets. The areal averages provide an estimate for what the integral of a model representing the behavior of the environmental variable should be over a particular district and the point values indicate the estimated value at particular locations. Thus, the integral of the fitted model over a district of the grid should approximate the value provided by the areal averages while also approximating the value provided by the point data for locations which are provided by the point data.

Abstract: Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.

Abstract: A method and system for decontaminating raw yield maps by combining filters with spatial outlier detectors is provided. In an embodiment, the method comprises receiving over a computer network electronic digital data comprising first yield data representing crop yields harvested from an agricultural field; applying one or more filters to the first yield data to identify, from the first yield data, first outlier data; generating first filtered data from the first yield data by removing the first outlier data from the first yield data; identifying, in the first filtered data, second outlier data representing outlier values based on one or more outlier characteristics; generating second outlier data from the first filtered data by removing the second outlier data from the first filtered data; generating and causing displaying on a mobile computing device a graphical representation of the crop yields harvested from the agricultural field using only the second outlier data.

Abstract: In an embodiment, a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.

Abstract: A computer-implemented method of targeting grower fields for crop yield lift is disclosed. The method comprises receiving, by a processor, crop seeding rate data and corresponding crop yield data over a period of time regarding a group of fields associated with a plurality of grower devices; receiving, by the processor, a current seeding rate for a grower's field associated with one of a plurality of grower devices; determining, whether the grower's field will be responsive to increasing a crop seeding rate for the grower's field from the current seeding rate to a target seeding rate based on the crop seeding rate data and corresponding crop yield data; preparing, in response to determining that the grower's field will be responsive, a prescription including a new crop seeding rate and a specific hybrid to be implemented in the grower's field.

Abstract: A method for building and using soil models that determine soil properties from soil spectrum data is provided. In an embodiment, building soil model may be accomplished using soil spectrum data received via hyperspectral sensors from a land unit. A processor updates the soil spectrum data by removing interference signals from the soil spectrum data. Multiple ground sampling locations within the land unit are then determined based on the updated soil spectrum data. Soil property data are obtained from ground sampling at the ground sampling locations. Soil models that correlate the updated soil spectrum data with the soil property data are created based on the updated soil spectrum data and the soil property data. The soil models are sent to a storage for future use.

Abstract: Techniques are provided for receiving a first set of historical agricultural data and a second set of historical agricultural data; generating a plurality of projected target yield ranges using the first set and the second set of historical agricultural data by generating a historic yield distribution; generating one or more yield ranking scores for one or more fields of a grower using the first set of historical agricultural data, and assigning a projected target yield range of the plurality of projected target yield ranges to each of the one or more fields based on the one or more yield ranking scores to generate assigned projected target yield ranges; receiving a third set of historical agricultural data comprising seed optimization data, and generating a recommended change in seed population or a recommended change in seed density; causing displaying the yield improvement recommendation for each of the one or more fields.

Abstract: In an embodiment, a computer-implemented method of selecting sampling locations in a field is disclosed. The method comprises receiving, by a processor, input data including a map for a management zone in a field indicating one or more values of a set of agricultural characteristics for each of a plurality of locations in the management zone. The method further comprises identifying, by the processor, a set of values for the set of agricultural characteristics for each of a group of locations in the management zone based on map. In addition, the method comprises normalizing a set of model values for the set of agricultural characteristics used by an agricultural modeling tool and the set of values of the set of agricultural characteristics for each of the group of locations in the management zone.

Abstract: In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.

Abstract: In an embodiment, a computer-implemented method of selecting locations in a field for treatment sampling is disclosed. The method comprises receiving, by a processor, input data including a number T of treatments applied to a field, a number L of treatment locations for each treatment, a list of treatment polygons within the field, and a map for the field indicating one or more values of a set of design parameters corresponding to environment factors for each of a plurality of locations in the field. The method also comprises computing, by the processor, an environment class index for each of a group of locations in the list of treatment polygons based on the map; distributing the list of treatment polygons to the T treatments based on the computed indices; and selecting, for each of the T of treatments, L treatment locations from the group of locations in the treatment polygons distributed to the treatment.

Abstract: A system and processing methods for refining a convolutional neural network (CNN) to capture characterizing features of different classes are disclosed. In some embodiments, the system is programmed to start with the filters in one of the last few convolutional layers of the initial CNN, which often correspond to more class-specific features, rank them to hone in on more relevant filters, and update the initial CNN by turning off the less relevant filters in that one convolutional layer. The result is often a more generalized CNN that is rid of certain filters that do not help characterize the classes.

Abstract: Described herein are systems and method for monitoring and controlling field operations including planting and harvesting operations. In one embodiment, a data processing system of a machine includes a data transfer and processing module that communicates bi-directionally with different types of controllers and sensors mounted on the machine or an implement attached to the machine. The data transfer and processing module is configured to execute instructions to receive signals from these controllers and sensors, process these signals, and generate data for monitoring and controlling field operations of the machine. At least one display device is coupled to the data transfer and processing module. The at least one display device displays the data for monitoring and controlling field operations of the machine or implement to a user or operator.

Abstract: A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water. A flow simulation is performed to determine regions of the field that are likely to pool water after rainfall based on precipitation data, elevation data, and soil property data of the field. A graph of vertices representing the pixels and edges representing connections between neighboring pixels is generated. The probability of each pixel within the graph being ponding water is set based on the probability pixel being water, the likelihood that water will pool in the area represented by the pixel, the probability of neighboring pixels being ponding water, and a cropland mask that identifies which pixels correspond to cropland.