Disclosed is a method of automated crop monitoring based on the processing and analysis of a large number of high resolution aerial images that map an area of interest using computer vision and machine learning techniques. The method comprises receiving 120 or retrieving image data containing a plurality of high resolution images of crops in an area of interest for monitoring, identifying 130 one or more crop features of each crop in each image, determining 140, for each identified crop feature, one or more crop feature attributes, and generating or determining 160 one or more crop monitoring outputs based, at least in part, on the crop features and crop feature attributes. Also disclosed is a method generating field camera specific training data for the machine learning model used to analyse the received image data.

Disclosed is a method of automated crop monitoring based on the processing and analysis of a large number of high resolution aerial images that map an area of interest using computer vision and machine learning techniques. The method comprises receiving 120 or retrieving image data containing a plurality of high resolution images of crops in an area of interest for monitoring, identifying 130 one or more crop features of each crop in each image, determining 140, for each identified crop feature, one or more crop feature attributes, and generating or determining 160 one or more crop monitoring outputs based, at least in part, on the crop features and crop feature attributes. Also disclosed is a method generating field camera specific training data for the machine learning model used to analyse the received image data.

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.

A row unit of an agricultural implement includes an opening system configured to engage soil to form a furrow, sensors configured to detect a soil tightness, soil conditions, operational conditions, or a combination thereof, and a closing system configured to close the furrow. The closing system includes a first closing disc configured to engage the soil and close the furrow and a second closing disc configured to engage the soil and close the furrow. The row unit also includes a controller configured to receive feedback from the sensors and to control a position, an orientation, or both, of the first closing disc, the second closing disc, or both, in response to feedback from the sensors.

A row unit of an agricultural implement includes an opening system configured to engage soil to form a furrow, sensors configured to detect a soil tightness, soil conditions, operational conditions, or a combination thereof, and a closing system configured to close the furrow. The closing system includes a first closing disc configured to engage the soil and close the furrow and a second closing disc configured to engage the soil and close the furrow. The row unit also includes a controller configured to receive feedback from the sensors and to control a position, an orientation, or both, of the first closing disc, the second closing disc, or both, in response to feedback from the sensors.

A farming machine is configured to identify and compensate for occlusions in the field of view of its image acquisition system. To do so, the machine captures an image using a first set of capture parameters associated with a first set of treatment results. The farming machine identifies an occlusion in the first image that obstructs a portion of the first image and determines occlusion characteristics representative of the occlusion based on image data in the first image. The farming machine compensates for the identified occlusion based on the occlusion characteristics. The farming machine captures a second image using modified set of capture parameters that compensate for the occlusion. The second image is associated with a second set of treatment results. The farming machine transmits the second set of treatment results to a manager of the farming machine.

A farming machine is configured to identify and compensate for occlusions in the field of view of its image acquisition system. To do so, the machine captures an image using a first set of capture parameters associated with a first set of treatment results. The farming machine identifies an occlusion in the first image that obstructs a portion of the first image and determines occlusion characteristics representative of the occlusion based on image data in the first image. The farming machine compensates for the identified occlusion based on the occlusion characteristics. The farming machine captures a second image using modified set of capture parameters that compensate for the occlusion. The second image is associated with a second set of treatment results. The farming machine transmits the second set of treatment results to a manager of the farming machine.

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.