Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.

A system for detecting material accumulation relative to rotating ground-penetrating tools of an agricultural implement includes an agricultural implement having a frame and at least one rotating ground-penetrating tool supported relative to the frame. The rotating ground-penetrating tool(s) is configured to penetrate into the ground to a given penetration depth. The system also includes a tool speed sensor configured to provide data indicative of a rotational speed of the ground-penetrating tool(s) and a computing system communicatively coupled to the tool speed sensor. The computing system is configured to monitor the rotational speed of the ground-penetrating tool(s) based on the data provided by the tool speed sensor, determine a threshold rotational speed based at least in part on the penetration depth of the ground-penetrating tool(s), and determine when the rotational speed of the ground-penetrating tool(s) falls below the threshold rotational speed.

A system for detecting material accumulation relative to rotating ground-engaging tools includes an agricultural implement having a frame and first and second ground-engaging tools supported relative to the frame, with the first ground-engaging tool corresponding to a different tool type than the second ground-engaging tool. The system also includes first and second speed sensor configured to provide data indicative of the rotational speeds of the first and second ground-engaging tools, respectively. In addition, the system includes a computing system communicatively coupled to the first and second speed sensors. The computing system is configured to monitor the rotational speeds of the ground-engaging tools based on the data provided by the speed sensors, determine a speed correlation between the rotational speed of the first ground-engaging tool and the rotational speed of the second ground-engaging tool, and determine when the speed correlation differs from a speed correlation threshold associated with the ground-engaging tools.

Control zones are dynamically identified on a thematic map and work machine actuator settings are dynamically identified for each control zone. A position of the work machine is sensed and actuators on the work machine are controlled based on the control zones that the work machine is in, and based upon the settings corresponding to the control zone. When modification criteria are met, an actuator setting in a control zone can be modified, during operation. The control zone is then divided on a near real-time display into a harvested portion of the control zone, and a new control zone that has yet to be harvested, and that has the modified actuator setting value. A record is then generated and stored, for the harvested control zone, and for the new control zone.

An agricultural zone management system and methods where a variable rate prescription (VRP) includes a plurality of equipment zones of the agricultural field that are generated based on at least one treatment dimension of a farm implement to be used in the agricultural field, and each one of the equipment zones is further defined based on one or more of an intended direction of travel of the farm implement in the agricultural field, and an intended travel path of the farm implement in the agricultural field. In addition, a treatment plan, such as a treatment rate, can be generated based on the needs of plants, soil or the like in each equipment zone.

A plough comprising: a plough body; an actuator mechanism that is configured to adjust a pitch angle of the plough body; and a controller. The controller is configured to: determine an actuator-control-signal for setting the pitch angle of the plough body based on control-data; and provide the actuator-control-signal to the actuator mechanism.

A computing system includes a processor and a non-transitory, computer-readable medium including instructions that, when executed by the processor, causes the computing system to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device. A computer-implemented method includes receiving a machine data set; processing the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmitting the variety profile index values to a client computing device. A non-transitory computer-readable medium includes instructions stored thereon that, when executed by one or more processors, cause a computer to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device.

Systems, methods, and apparatuses for calibrating a first meter of an air cart include determining a calibration factor using a second meter. The second meter may be located with the air cart or remotely from the air cart. An amount of material dispensed from the second meter is used to determine the calibration factor. A number of cycles of operation of the second meter may also be used to determine the calibration factor. The calibration factor may be provided via a wired or wireless connection to a controller operable to control operation of the first meter.

The work vehicle includes a vehicle body, a cabin mounted on the vehicle body and having a roof, and a positioning device located above the cabin and configured to detect a position of the vehicle body on the basis of a signal transmitted from a positioning satellite. The roof has a roof front end located in a forefront portion in a front-rear direction; and a roof uppermost end located behind the roof front end and located higher than the roof front end. A position of the positioning device is lower than the roof uppermost end.

A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed. A computer-implemented method includes receiving a machine data set; processing the machine data set with a trained machine-learned model to generate predicted variety profile index values; and causing a visualization to be displayed. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by one or more processors, cause a computer to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed.