Abstract: In an embodiment, a computer-implemented method of detecting infected objects from large field-of-view images is disclosed. The method comprises receiving, by a processor, a digital image capturing multiple objects; generating, by the processor, a plurality of scaled images from the digital image respectfully corresponding to a plurality of scales; and computing a group of feature matrices for the digital image. The method further comprises, for each of the plurality of scaled images. selecting a list of candidate regions from the scaled image each likely to capture a single object; and for each of the list of candidate regions, performing the following steps: mapping the candidate region back to the digital image to obtain a mapped region; identifying a corresponding portion from each of the group of feature matrices based on the mapping; and determining whether the candidate region is likely to capture the single object infected with a disease based on the group of corresponding portions.

Abstract: A system and method for monitoring an agricultural implement. The system includes a monitor device, a communication module and a display device. The monitor device is in electrical communication with a plurality of sensors monitoring the operation of agricultural implement. The implement sensors generate “as-applied” data. The as-applied data is processed and transmitted to a display device via a communication module. The display device renders maps representing the as-applied data. The generated maps may be accessed and displayed as map overlays on a display device with a common view characteristic.

Abstract: A method and system for generating probabilistic estimates of precipitation intensity from radar reflectivity measurements is provided. In an embodiment, an agricultural intelligence computer system receives radar reflectivity measurements for a particular location from an external data source. The agricultural intelligence computer system constructs a probability distribution of drop sizes describing the probability that the precipitation included drops of various sizes based on the radar reflectivity measurements. The agricultural intelligence computer system samples a plurality of values from the probability of distribution of drop sizes and uses the plurality of values and the radar reflectivity measurements to compute a plurality of rainfall rates. Based on the plurality of rainfall rates, the agricultural intelligence computer system constructs a probability distribution of rainfall rates for the particular location.

Abstract: In an embodiment, a method comprises: receiving pre-planting data representing a lower bound date value and an upper bound date value of dates for a pre-planting application of fertilizer to an agricultural field; side-dressing data representing a lower bound date value and an upper bound date value of dates for a side-dressing application; fertilizer cost data representing a cost of a fertilizer application; labor cost data representing a cost of applying fertilizer to the field; and expected profit data. Based on the received data, one or more penalty constraints are determined. Based on the received data, a fertilizing schedule is generated. The schedule comprises the one or more valid calendar dates on which fertilizing the agricultural field is recommended and the one or more valid fertilizer amounts to be applied to the agricultural field on the one or more valid calendar dates to maximize a yield from the agricultural field.

Abstract: A method for estimating growth stage threshold values for a specific hybrid seed at a specific geo-location using historical growth stage data and observed growth stage data comprises using a server computer system, storing a historical crop growth model of one or more hybrid seeds measured from one or more fields over a particular period of time. The historical crop growth model includes growth stage threshold estimates for one or more hybrid seeds. The server computer system receives, via a network, one or more digital measurement values specifying one or more observed growth stage values for a particular hybrid seed at a particular field over a particular period of time. The server computer system transforms the growth stage thresholds into growth stage duration values for the historical crop data and the observed crop data.

Abstract: In an embodiment, yield data representing yields of crops that have been harvested from an agricultural field and field characteristics data representing characteristics of the agricultural field is received and used to determine a plurality of management zone delineation options. Each option, of the plurality of management zone delineation options, comprises zone layout data for an option. The plurality of management zone delineation options is determined by: determining a plurality of count values for a management class count; generating, for each count value, a management delineation option by clustering the yield data from and the field characteristics data, assigning zones to clusters, and including the zones in a management zone delineation option. One or more options from the plurality of management zone delineation options are selected and used to determine one or more planting plans. A graphical representation of the options and the planting plans is displayed for a user.

Abstract: Systems, methods and apparatus are provided for soil testing. In some embodiments, a soil sample quality criterion is determined and associated with the soil sample. In some embodiments a soil characteristic measurement is additionally taken and associated with the soil sample. In some embodiments, the soil sample and its associated data are associated with a container in which the soil sample is placed.

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: Systems and methods for implementing a plot in a field based on the location of a marker placed in an application prescription map prior to a crop input application operation are disclosed. The plot defines a plot region. The plot region is defined during the crop input application operation based on the marker location.

Abstract: A method for estimating adjusted rainfall values for a set of geo-locations using agricultural data comprises using a server computer system that receives, via a network, agricultural data records that are used to estimate rainfall values for the set of geo-locations. Within the server computer system, rainfall calculation instructions receive digital data including observed radar and rain-gauge agricultural data records. The computer system then aggregates the agricultural data records and creates and stores the agricultural data sets. The agricultural data records are then transformed into one or more distribution sets. The distribution sets are then used to determine regression parameters for a digital rainfall regression model. The digital rainfall regression model then is used to estimate adjusted rainfall values for a new set of geo-locations. The server computer system then generates a digital image that includes the geo-locations and the adjusted rainfall values.

Abstract: A method and system for estimating uncertainties in radar based precipitation estimates is provided. In an embodiment, gauge measurements at one or more gauge locations are received by an agricultural intelligence computer system. The agricultural intelligence computer system obtains precipitation estimates for the one or more gauge locations that correspond to the gauge measurements and computes the differences between the precipitation estimates and the gauge measurements. Using the precipitation estimates and the computed differences, the agricultural intelligence computer system then models a dependence of the uncertainty in the precipitation estimates on the value of the precipitation estimates.

Abstract: A method for agronomic and agricultural monitoring includes designating an area for imaging, determining a flight path above the designated area, operating an unmanned aerial vehicle (UAV) along the flight path, acquiring images of the area using a camera system attached to the UAV, and processing the acquired images.

Abstract: A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for sowing hybrid seeds in a field is provided. In an embodiment, determining and presenting seeding rate recommendations for a field may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including hybrid seed type, and sowing row width. Using digitally programmed seeding query logic, within the server computer system, receiving digital data representing planting parameters including hybrid seed type information and sowing row width. The seeding query logic then retrieves a set of one or more seeding models from an electronic digital seeding data repository based upon the planting parameters. Each of the seeding model retrieved contain a regression model for the hybrid seed type modeling a relationship between plant yield and seeding rate on a specific field.

Abstract: A method for estimating soil properties within a field using hyperspectral remotely sensed data is provided. In an embodiment, estimating soil properties may be accomplished using a server computer system that receives, via a network, soil spectrum data records that are used to predict soil properties for a specific geo-location. Within the server computer system a soil preprocessing module receives one or more soil spectrum data records that represent a mean soil spectrum of a specific geo-location of a specified area of land. The soil preprocessing module then removes interference signals from the soil spectrum data, creating a set of one or more spectral bands. By removing interference signals, the spectral bands are not erroneously skewed from effects such as baseline drift, particle deviation, and surface heterogeneity. A soil regression module inputs the one or more soil spectral bands and predicts soil property datasets.

Abstract: In an embodiment, a system receives first data comprising precipitation gauge measurements at a plurality of gauge locations. The system obtains second data comprising radar based precipitation estimates at the plurality of gauge locations. For each radar based precipitation value at the plurality of gauge locations, the system identifies one or more corresponding precipitation gauge measurement, computes a gauge radar differential value for the radar based precipitation estimate, and stores the gauge radar differential value with location data identifying a corresponding location of the plurality of gauge locations. The system then obtains a particular radar based precipitation estimate at a non-gauge location.

Abstract: A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data: determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

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, 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 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: A soil imaging system having a work layer sensor disposed on an agricultural implement to generate an electromagnetic field through a soil area of interest as the agricultural implement traverses a field. A monitor in communication with the work layer sensor is adapted to generate a work layer image of the soil layer of interest based on the generated electromagnetic field. The work layer sensor may also generate a reference image by generating an electromagnetic field through undisturbed soil. The monitor may compare at least one characteristic of the reference image with at least one characteristic of the work layer image to generate a characterized image of the work layer of interest. The monitor may display operator feedback and may effect operational control of the agricultural implement based on the characterized image.