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.

In one aspect, a crop input applicator is provided. In one aspect, the applicator comprises a valve (e.g., voice coil valve) disposed to deposit liquid on or near a seed furrow and/or on or near a seed. In one aspect, the applicator includes one or more seed sensors disposed to detect passage of seeds.

In one aspect, a crop input applicator is provided. In one aspect, the applicator comprises a valve (e.g., voice coil valve) disposed to deposit liquid on or near a seed furrow and/or on or near a seed. In one aspect, the applicator includes one or more seed sensors disposed to detect passage of seeds.

The disclosure relates 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 identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.

The disclosure relates 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 identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.

A soil apparatus (e.g., seed firmer) having a locking system is described herein. In one embodiment, the soil apparatus includes a lower base portion for engaging in soil of an agricultural field, an upper base portion, and a neck portion having protrusions to insert into the lower base portion of a base and then lock when a region of the upper base portion is inserted into the lower base portion and this region of the upper base portion presses the protrusions to lock the neck portion to the upper base portion.

A soil apparatus (e.g., seed firmer) having a locking system is described herein. In one embodiment, the soil apparatus includes a lower base portion for engaging in soil of an agricultural field, an upper base portion, and a neck portion having protrusions to insert into the lower base portion of a base and then lock when a region of the upper base portion is inserted into the lower base portion and this region of the upper base portion presses the protrusions to lock the neck portion to the upper base portion.

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

In one aspect, a system for controlling the operation of a residue removal device of a seed-planting implement may include a residue removal device configured to remove residue from a path of the seed-planting implement. The system may also include a sensor configured to capture data indicative of a residue characteristic associated with a portion of the field within a detection zone positioned forward of the residue removal device relative to a direction of travel of the seed-planting implement. Furthermore, the system may include a controller communicatively coupled to the sensor. As such, the controller may be configured to monitor the residue characteristic associated with the portion of the field within the detection zone based on data received from the sensor. Additionally, the controller may be further configured to control the operation of the residue removal device based on the monitored residue characteristic.