A method of spreading granular material by a spreader utilizes first and second rotatingly drivable centrifugal disks arranged side by side, and includes steps of: detecting a field-internal spreading boundary, which extends ahead of the spreader in a direction of movement of the spreader and which necessitates an adaptation of distribution characteristics of the spreader during traveling on at least one tramline; reducing an amount of granular material applied to the second centrifugal disk, which is part of the spreader and which faces the field-internal spreading boundary, during traveling through a first range close to the field-internal spreading boundary; and increasing the amount of granular material applied to the first centrifugal disk, which is part of the spreader and which faces away from the field-internal spreading boundary, during traveling through the first range close to the field-internal spreading boundary.

In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

An agricultural machine includes a seeding system having a seed transport mechanism configured to transport a seed along a transport route, a seed sensor configured to sense presence of the seed at a first location along the transport route, and a motor configured to drive movement of the seed transport mechanism to transport the seed from the first location to a second location in which the seed is released from the seed transport mechanism. A processing system is configured to track a position of the seed along the transport route based on an indication of the sensed presence of the seed at the first location and detected movement of the seed transport mechanism, and generate a motor operating parameter based on the tracked position of the seed and a target parameter for releasing the seed. The motor of the seeding system is operated based on the motor operating parameter.

In one aspect, a system for controlling the operation of a seed-planting implement may include a furrow-forming tool configured to form a furrow in soil present within a field. Furthermore, the system may include a sensor configured to capture data indicative of a topographical profile of the soil within the field. Additionally, a controller of the disclosed system may be configured to identify a topographical feature within the field based on the data received from the sensor. Furthermore, the controller may be configured to determine a position of the furrow-forming tool relative to the identified topographical feature. Additionally, the controller may be configured to initiate a control action to adjust the position of the furrow-forming tool when it is determined that the relative position between the furrow-forming tool and the identified topographical feature is offset from a predetermined positional relationship defined for the furrow-forming tool.

A particulate material metering system includes a controller configured to determine a radius of curvature of a path of a lead vehicle coupled to an agricultural implement. The controller is configured to determine a lead time of the lead vehicle relative to the agricultural implement based on a speed of the lead vehicle and/or the agricultural implement. The controller is configured to determine a radius of curvature of a path of the agricultural implement based on the radius of curvature of the path of the lead vehicle at a determination time, in which the determination time is a current time minus an offset time, and the offset time is based on the lead time. The controller is configured to determine target particulate material flow rates of respective metering devices of the particulate material metering system based on the radius of curvature of the path of the agricultural implement.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include calculating an amount of light originating from a light source sensed at a location in which an agricultural object is interposed between the light source and an image capture device, causing emission of an obscurant, and directing the obscurant to a region interposed between the light source and the agricultural object.

Disclosed herein are various embodiments of dual seed meters having a metering housing having first and second seed disks with a plurality of openings arranged along a curved path of each disk, wherein the disks overlap to form a convergence region such that seed traveling along the curved paths is delivered to a single point. Certain implementations also have a vacuum chamber defined between a first wall of the metering housing and the first and second seed disks and a seed chamber defined between a second wall of the metering housing and the first and second seed disks.

A method for measuring photosynthetically active radiation (PAR), and particularly fraction-absorbed PAR (faPAR) measurements, which shows a relationship between how much energy is available compared with how much energy is actually used, or absorbed, by plants in a region of interest, can include calibrating multiband image data to generate reflectance-calibrated values for each band of the multiband image data. The weighted reflectance data of the bands can be combined, along with down-welling light data captured at the time of the multi-spectral images to generate a faPAR image. In some cases, faPAR is generated using a ratio of up-welling PAR (uPAR) to down-welling PAR (dPAR). The dPAR can be generated using partially-reflectance-calibrated data and fully-reflectance-calibrated data and the uPAR can be generated using the partially-reflectance-calibrated data.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.