Systems, devices, and methods for detecting agrochemicals in environments associated with agricultural equipment are described. Certain agrochemicals that are formulated for being detected using the systems, devices, and methods disclosed herein are also described. The devices, systems, and methods disclosed herein are generally configured to use spectral characteristics to detect agrochemicals in an environment associated with agricultural equipment. The spectral characteristics can be analyzed in various ways to provide different types of information about the agrochemicals and/or the environment.

A boom sprayer includes any number of components to treat plants as the boom sprayer travels through a plant field. The components take actions to treat plants or facilitate treating plants. The boom sprayer includes any number of sensors to measure the state of the boom sprayer as the boom sprayer treats plants. The boom sprayer includes a control system to generate actions for the components to treat plants in the field. The control system includes an agent executing a model that functions to improve the performance of the boom sprayer treating plants. Performance improvement can be measured by the sensors of the boom sprayer. The model is an artificial neural network that receives measurements as inputs and generates actions that improve performance as outputs. The artificial neural network is trained using actor-critic reinforcement learning techniques.

Implementations set forth herein relate to using fiducial markings on one or more localized portions of an agricultural apparatus in order to generate local and regional data that can be correlated for planning and executing agricultural maintenance. An array of fiducial markings can be disposed onto plastic mulch that surrounds individual crops, in order that each fiducial marking of the array can operate as a signature for each individual crop. Crop data, such as health and yield, corresponding to a particular crop can then be stored in association with a corresponding fiducial marking, thereby allowing the certain data for the particular crop to be tracked and analyzed. Furthermore, autonomous agricultural devices can rely on the crop data, over other sources of data, such as GPS satellites, thereby allowing the autonomous agricultural devices to be more reliable.

A boom suspension system may comprise a center frame operably connected to a main frame with linkages configured for vertical movement. A sensor may be operably connected to a pair of boom structures, which may extend laterally outward from opposing sides of the center frame. A controller may be configured to receive input data from the sensor and determine forces or flow rate to tilt cylinders, which may be coupled between each boom structure and the center frame. Each tilt cylinder may be operably connected to a hydraulic circuit, which may comprise a flow control mode and a pressure control mode determined by the controller. The hydraulic circuit may comprise a first set of valves in parallel with a second set of valves. Each set of valves may comprise a solenoid valve in series with a pressure regulating valve and a pressure sensor disposed on either side of each solenoid valve.

Described herein are implements and applicators for placement of fluid applications with respect to agricultural plants of agricultural fields. In one embodiment, a fluid applicator for applying fluid to plants in rows in a field includes a frame, at least one applicator arm disposed in a rhizosphere of plants during fluid flow through the applicator, and a fluid conduit connected to the frame and disposed in the row between plants to deliver fluid to the row between plants.

Apparatus for spreading fluids and in particular fertilizers, pesticides and similar fluids, with a connection for attaching said apparatus to a machine, with a tubular body for conducting the fluid, and with a first outlet device via which the fluid can be discharged. The outlet device can be fixed at various positions in a longitudinal direction of the tubular body such that a position of the outlet device can be selected relative to the tubular body.

Embodiments of the inventive concept provide a portable organic entity termination applicator unit. The applicator unit can include an organic herbicide reservoir or can otherwise be an herbicideless applicator unit. The applicator unit can include an outer housing, a cold water supply line, an organic herbicide reservoir to hold organic herbicide, an herbicide adjustment valve for controlling a liquid mixture, a heater core to pre-heat the herbicide water liquid mixture, and a delivery pipe to expel the heated liquid. The applicator unit can include a fuel canister receptacle to receive a fuel canister. The heater core may heat the liquid using fuel stored in the fuel canister.

A map generator generates a replanting map designating a particular area in a field in which it is recommended to add additional seeds. A function of the agricultural machine is then controlled based at least in part on the replanting map so as to facilitate planting additional seeds in the designated particular area.

In order to achieve a more effective application of a crop efficiency product, a computer-implemented method is provided for applying a crop efficiency product to at least one crop in a field. The method comprises the steps of collecting remotely-sensed data of the field before an application of the crop efficiency product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

Systems and methods automate the design and execution of randomized experiments. Portions of a field are planted using an agricultural vehicle configured to randomly vary planting parameters when planting a portion of the field. A resulting crop outcome across each portion or sub-portion of the field is observed. A training set of data is generated that includes the varied planting parameters and the associated crop outcomes for each portion of the field. A machine-learned model is trained using the training set of data and is configured to predict a crop outcome for a portion of the field based on historical and forecast conditions and a set of planting parameters applied to a portion of the field. For subsequent iterations, for a target portion of the field, the machine-learned model can be applied to identify a set of planting parameters for planting the target portion of the field to optimize a desired crop outcome.