A mobile agricultural machine includes a row unit having a furrow opener mounted to the row unit and configured to engage a surface of ground over which the mobile agricultural machine travels to open a furrow in the ground. A furrow closer is mounted to the row unit behind the furrow opener relative to a direction of travel of the mobile agricultural machine and is configured to engage the surface of the ground to close the furrow. An image sensor system is mounted to the row unit and configured to sense characteristics of residue and seeds in the furrow opened by the furrow opener and generate a sensor signal indicative of the characteristics. The mobile agricultural machine can further include a control system configured to generate a residue/seed characteristic indicator corresponding to the sensed characteristics and to generate an action signal to control an action of the mobile agricultural machine based on the residue/seed characteristic indicator.

A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map.

A system for analyzing soil content of a field includes a data acquisition unit configured to detect gamma spectra of each of a plurality of soil samples, wherein a surface area of the field is divided into a plurality of portions and the plurality of soil samples comprises at least one soil sample from each of the plurality of portions, a navigation unit configured to detect geographic coordinates of each of the plurality of soil samples, a data analysis unit configured to associate the detected gamma spectra of each of the plurality of soil samples with the geographic coordinates of the soil sample and determine a weight percent of at least one element within each of the soil samples based on the detected gamma spectra, and an element content map unit configured to generate a map indicating concentration of the at least one element within the soil of the field.

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. In an embodiment, a computer-implemented data processing method providing an improvement in efficient calculation of digital data representing physical properties of agricultural fields, the method comprising receiving digital input specifying a request to display a map image of a specified agricultural field for a particular day; in response to receiving the input, calculating an interpolated digital image of the specified agricultural field with a plurality of different field properties, by: dividing a digital map of the specified field into a plurality of grids each having a same size and a same area; obtaining, from digital storage, a plurality of data for the different field properties and assigning the data as covariates; grouping the grids into a specified number of clusters based on values of the covariates; pseudo-randomly selecting a specified number of one or more sample values in each of the clusters; evaluating a digital fertility model using the sample values and storing a plurality of output values from the digital fertility model; interpolating a plurality of model values for the grids; generating and causing displaying a visual graphical image of the specified agricultural field including color pixels corresponding to each of the model values.

A soil analysis system is provided for an agricultural vehicle and includes a sensor apparatus, a controller, and a display device. The sensor apparatus includes a location sensor configured to determine a location of the agricultural vehicle; and an infrared sensor configured to collect infrared spectra from soil at the location. The controller is configured to determine a soil type based on the location; select at least one nutrient calibration curve based on the soil type at the location; analyze the infrared spectra according to the at least one nutrient calibration curve to generate at least one estimated nutrient value for the soil at the location; and generate display commands representing the at least one estimated nutrient value. The display device is configured to generate a first display representing the at least one estimated nutrient value based on the display commands.

A system for identifying objects present within a field across which an agricultural vehicle is traveling includes a transceiver-based sensor configured to capture point cloud data associated with a portion of the field present within a field of view of the transceiver-based sensor as the agricultural vehicle travels across the field. Additionally, the system includes a display device and a controller communicatively coupled to the transceiver-based sensor and the display device. The controller, in turn, is configured to analyze the captured point cloud data to create a sparse point cloud identifying at least one of a crop row or a soil ridge located within the portion of the field present within the field of view of the transceiver-based sensor. Furthermore, the controller is configured to initiate display of an image associated with the sparse point cloud on the display device.

A modular system includes a hub and a set of modules removably coupled to the hub. The modules are physically coupled to the frame relative to each other so that each module can operate with respect to a different row of a field. An individual module includes a sensor for capturing field measurement data of individual plants along a row as the modular system moves through the geographic region. An individual module further includes a treatment mechanism for applying a treatment to the individual plants of the row based on the field measurement data before the modular system passes by the individual plants. An individual module further includes a computing device that determines the treatment based on the field measurement data and communicates data to the hub. The hub is communicatively coupled to the modules, so that it may exchange data between the modules and with a remote computing system.

Described herein is a farm management system and related apparatus and operations including providing environmental monitoring and control systems, tracking seed sources, managing cultivation, growth, and harvest, improving enclosure operations, and managing system data. Example farm management systems may include a physical growing unit, a capacity control circuit structured to interpret a growing capacity description, and a growth support circuit structured to determine a planting instruction value in response to the growing capacity description. The physical growing unit may be responsive to the planting instruction value to initiate a growing support operation.

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.

A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.