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.

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.

An active substance supply system for an agricultural sprayer has a main liquid circuit which includes a reservoir for a main liquid and permits circulation of the main liquid, where the main liquid circuit includes one or more distribution sections which are each configured to distribute to several dispensing elements a spray liquid to be dispensed, and a feed system which is connected to the main liquid circuit and in which the main liquid is mixed with at least one separately supplied active substance to form a liquid active substance mixture, where the active substance supply system is configured to introduce the active substance mixture into the one or more distribution sections at several places.

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.

An automated implement control system includes one or more distance sensors configured for coupling with an agricultural implement. The one or more distance sensors are configured to measure a ground distance and a canopy distance from the one or more sensors to the ground and crop canopy, respectively. An implement control module is in communication with the one or more distance sensors. The implement control module controls movement of the agricultural implement. The implement control module includes a confidence module configured to determine a ground confidence value based on the measured ground distance and a canopy confidence value based on the measured canopy distance. A target selection module of the implement control module is configured to select one of the measured ground or canopy distances as a control basis for controlling movement of the agricultural implement based on the comparison of confidence values.

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.

The present invention is concerned with the predicting of crop protection product residues in plants or parts of plants intended for human and/or animal consumption, preferably in fruit and/or vegetables. The present invention provides a method, a device, a system and a computer program product for prediction of crop protection product residues.

A portable system for testing or demonstrating an agricultural implement includes a case (104) carrying a power supply (206), a plurality of electrical couplers (120) configured to receive wiring harnesses associated with test devices, a simulator module (214) configured to simulate at least one operating parameter of the agricultural implement on which test devices are carried, and a control system. The control system includes a graphical user interface (110) and processing circuitry operably electrically coupled to the graphical user interface (110) and to the wiring harnesses. The processing circuitry is configured to monitor and display information pertaining to operation of the test devices. A method for testing or demonstrating an agricultural implement includes connecting a test device an electrical coupler of the portable system, sending a control signal to the test device, and monitoring performance of the test device with the control system. The control signal is based at least in part on the data input.

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.

A system for an agricultural vehicle includes a boom assembly and a nozzle assembly positioned along the boom assembly. A position sensor is associated with the boom assembly. A field sensor is also associated with the nozzle assembly. A computing system is operably coupled with the nozzle assembly, the position sensor, and the field sensor. The computing system is configured to detect a target within a field based on data from the field sensor, determine a boom deflection model based on data from the position sensor, and activate the nozzle assembly to apply an agricultural product to the target based on the boom deflection model.