An automated farming system includes a frame. The frame includes a fixed base, a beam, and a support. A farming implement support extends from the beam and moves up and down in relation to the beam. The farming implement support moves along a length of the beam. The movable support includes a propulsion system and is configured to rotate around the fixed base. Movement of the farming implement support and the movable support allows for high density planting of crops in hexagonal patterns and/or a continuous spiral pattern.

A mobile vehicle having a burner assembly for movement over a ground surface and directing flame onto the ground surface to incinerate the materials on the ground surface and growing therefrom and including a water spray for reducing air pollution associated with the incineration.

A mobile vehicle having a burner assembly for movement over a ground surface and directing flame onto the ground surface to incinerate the materials on the ground surface and growing therefrom and including a water spray for reducing air pollution associated with the incineration.

Historical information is accessed that describes a previous state of a field, previous environmental conditions for the field, and previous farming machine actions performed in the field. A tank management model is applied to the historical information to determine expected weed densities within field portions and an amount of treatment fluid required to treat plants within the field. While the farming machine is treating plants in the field, current information describing a current state of the field is accessed. The tank management model is applied to the current information to determine updated weed densities within field portions not yet treated. The farming machine performs a modified plant treatment action based on the updated weed densities and a comparison of a remaining amount of treatment fluid within a tank of the farming machine and an updated amount of treatment fluid for treating plants within field portions not yet treated.

An agricultural machine includes a motor configured to drive a seeding system that is, itself, configured to meter and deliver seed from the agricultural machine. The agricultural machine also includes a sensor configured to sense a characteristic of the seeding system and a skip detector component, that receives the sensor signal and detects a seed skip in the seeding system. Based on the detected seed skip, a processing system generates an operating parameter of the motor, and controls the motor based on the operating parameter.

An agricultural machine includes a motor configured to drive a seeding system that is, itself, configured to meter and deliver seed from the agricultural machine. The agricultural machine also includes a sensor configured to sense a characteristic of the seeding system and a skip detector component, that receives the sensor signal and detects a seed skip in the seeding system. Based on the detected seed skip, a processing system generates an operating parameter of the motor, and controls the motor based on the operating parameter.

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.

Disclosed herein are an apparatus and method for providing a wide-area precision agriculture service based on collaboration between heterogeneous drones. The method for providing a wide-area precision agriculture service based on collaboration between heterogeneous drones includes transferring first mission information including photography of an entirety of arable land to a fixed-wing drone, receiving first drone data corresponding to the first mission information from the fixed-wing drone, and analyzing the entire agricultural arable land based on the first drone data, and transferring detailed mission information generated based on a result of analysis of the entire agricultural arable land to at least one rotary-wing drone.

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.