An illustrative smart sprayer for a precision agricultural implement includes a mount for coupling the sprayer to a modular tool arm of the implement, and a spray head, an airflow generator, a solenoid valve, and an inspirator mixer located within a containment shroud. The smart sprayer is one of alternative agricultural tools that can be selectively coupled to and be operated by a control system of the common platform precision agricultural implement, including a machine vision module for identifying and locating commodity plants, non-commodity plants, and positioning the agricultural tools laterally and vertically relative to commodity plant lines.

Historical and seasonal data is obtained by an agricultural work machine. The historical data provides historical values of agricultural characteristics, which may or may not be geolocated, and the seasonal data provides seasonal values of agricultural characteristics corresponding to a current season. A predictive map generator generates a predictive map that predicts an agricultural characteristic, such as crop state, at different locations in the field based on a relationship between the historical values of agricultural characteristics in the historical data and based on the seasonal values of agricultural characteristics in the seasonal data at those different locations. The predictive map can be output and used in automated machine control.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

Systems and methods are provided for use in processing image data associated with crop-bearing fields. One example computer-implemented method includes accessing a first data set including images associated with a field, where the images have a spatial resolution of about one pixel per at least about one meter, and generating, based on a generative model, defined resolution images of the field from the first data set. In doing so, the defined resolution images each have a spatial resolution of about X centimeters per pixel, where X is less than about 5 centimeters. The method also includes deriving index values for the field, based on the defined resolution images of the field, and predicting a characteristic (e.g., a yield, etc.) for the field based on the index values and, in some implementations, at least one environmental metric for the field.

Methods of determining apportionments for agricultural products (e.g., fixed amounts of seed) to one or more growing locations (e.g., agricultural fields) for a first growing season based upon growing performances of at least one of first growing locations or second growing locations different from or overlapping with the first growing locations can include remotely collecting intrinsic and extrinsic attributes for the second growing locations, the extrinsic attributes for a second growing season prior to the first growing season, determining aggregate commercial desirabilities for crop samples grown in the second growing locations, normalizing the aggregate commercial desirabilities to establish commercial desirability indices, training a controller to identify a subset of attributes selected from the intrinsic and extrinsic attributes and correlated with the commercial desirability indices, remotely collecting intrinsic and extrinsic attributes for the first growing locations, and predicting commercial desirability indices for the first growing locations based upon the identified attributes.

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 identifying an emitter of an agricultural projectile delivery system to calibrate a trajectory of an agricultural projectile to intercept a target, predicting a projectile impact site relative to the reference of alignment, determining a calibration parameter to align the projectile impact site and the target, and adjusting the trajectory based on the one or more calibration parameters.

An orientation control system for an agricultural implement includes a first sensor configured to be positioned at a left end portion of a frame. The first sensor is configured to emit a first output signal toward a soil surface and to receive a first return signal indicative of a first height of the left end portion. The orientation control system also includes a second sensor configured to be positioned at a right end portion of the frame. The second sensor is configured to emit a second output signal toward the soil surface and to receive a second return signal indicative of a second height of the right end portion. In addition, the orientation control system includes a controller configured to control first, second, and third actuators such that a difference between the first height and the second height is less than a threshold value.

In accordance with an example embodiment, a vehicle path planning system and a method for planning a path of a vehicle having a work tool are provided. The method includes determining an actual path through a work area, modifying the actual path with a margin to determine a modified path plan through the work area, and passing the work tool through the work area with the vehicle along the modified path plan.

Crop is routed past a sample window on an agricultural combine harvester. Light it is impinged on the crop from an illumination source and reflected radiation is directed to a sensor. The output of the sensor is indicative of various constituents in the harvested crop. The illumination source is controlled based on the temperature proximate the crop sample.

An agricultural harvesting machine includes crop processing functionality configured to engage crop in a field, perform a crop processing operation on the crop, and move the processed crop to a harvested crop repository, and a control system configured to identify a weed seed area indicating presence of weed seeds, and generate a control signal associated with a pre-emergence weed seed treatment operation based on the identified weed seed area.