As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Modern farming is currently being done by powerful ground equipment or aircraft that weigh several tons and treat uniformly tens of hectares per hour. Automated farming can use small, agile, lightweight, energy-efficient automated robotic equipment that flies to do the same job, even able to farm on a plant-by-plant basis, allowing for new ways of farming. A hybrid airship-drone has both passive lift provided by a gas balloon and active lift provided by propellers. A hybrid airship-drone may be cheaper, more stable in flight, and require less maintenance than other aerial vehicles such as quadrocopters. However, hybrid airship-drones may also be larger in size and have more inertia that needs to be overcome for starting, stopping and turning.

A method of generating a suggested optimal time for performing an agricultural operation in a field includes receiving data related to a current condition of a crop in the field. A predicted optimal time to perform the agricultural operation in the field is calculated using an agricultural model. Crowdsourcing data is received related to agricultural operations occurring within a defined area surrounding the field and within a defined preceding time period. The predicted optimal time to perform the agricultural operation is adjusted based on the crowdsourcing data, to generate the suggested optimal time to perform the agricultural operation. The suggested optimal time is communicated to a communicator located remote from the computing device and then displayed on the communicator.

An automated apparatus, method and system for projecting a control agent toward a recognised target for the purposes of agricultural cultivation or environmental management or various other applications; a source of the control agent adapted for use in connection with an environmental control function, an outlet incorporating at least one outlet orifice to direct the control agent emanating from the outlet orifice toward a target. An activation means is movable between an operative mode and an inoperative mode in which the outlet is effectively closed. A targeting mechanism movable on at least one independent control axis provided for selectively orienting the outlet orifice and thereby orienting the control agent in the operative mode. A first sensing system, a classification system, a control system in accordance with a predetermined control logic adapted to deliver doses of the control agent to the identified targets for the purposes of the environmental control function.

Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.

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 and configured to engage the surface of the ground to close the furrow. A furrow sensor system is mounted to the row unit and configured to sense characteristics relative to 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 determine a furrow quality metric corresponding to the furrow sensed by the furrow sensor system based on the sensor signal and generate an action signal to control an action of the mobile agricultural machine based on the furrow quality metric.

Described herein are technologies for mapping sensed positions of a toolbar of an agricultural implement, such as when the implement moves through a crop field. The technologies include a method including (1) sensing a position of the toolbar at a geographic location of the field, (2) matching the location of the field with a position in a map of the field corresponding to the location, and (3) associating the sensed position of the toolbar to the position in the map. In some embodiments, the method includes repeating the aforesaid operations for multiple geographic locations of the field and rendering an image of the map to be displayed in a GUI. Also, in some embodiments, the method includes rendering the image of the map with an image of a yield map of the field. In some embodiments, the method includes generating a topographic map based on the sensed toolbar positions.

Described herein are technologies for mapping sensed positions of operative rows of an agricultural implement, such as when the implement moves through a crop field. The technologies include a method including (1) sensing a position of an operative row at a geographic location of the field, (2) matching the location of the field with a position in a map of the field corresponding to the location, and (3) associating the sensed position of the row to the position in the map. In some embodiments, the method includes repeating the aforesaid operations for multiple geographic locations of the field and rendering an image of the map to be displayed in a GUI. Also, in some embodiments, the method includes rendering the image of the map with an image of a yield map of the field. In some embodiments, the method includes generating a topographic map based on the sensed row positions.

A system for providing a position for an agricultural vehicle. The system includes a first receiver structured to be coupled to a first vehicle, the first receiver configured to receive position correction information from an external source and determine a first position of the first receiver in three dimensions using the position correction information. The system also includes a second receiver structured to be coupled to a second vehicle and configured to determine a second position of the second receiver, wherein the first receiver is configured to determine the first position using the position correction information at a higher level of accuracy than the second receiver is configured to determine the second position. The system also includes one or more processing circuits, each processing circuit including a processor and a memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to determine a position of the second vehicle in three dimensions, including a vertical position of at least a portion of the second vehicle, using the position correction information received by the first receiver.

A work vehicle includes a location sensor configured to capture data indicative of a location of the work vehicle within a field. Additionally, the work vehicle includes a computing system communicatively coupled to the location sensor. In this respect, the computing system is configured to control an operation of the plurality of components as the work vehicle makes a pass across the field. Moreover, the computing system is configured to record a travel path of the work vehicle as the work vehicle makes the pass across the field based on data captured by the location sensor. In addition, the computing system is configured to analyze the recorded travel path to determine one or more geometric primitives of the recorded travel path. Furthermore, the computing system is configured to generate a swath line for the pass based on the determined one or more geometric primitives.