An agricultural harvester includes a frame configured to support a crop processing system and a sensor supported on the frame, with the sensor configured to capture data indicative of one or more subsurface soil layers present within the field across which the agricultural harvester is traveling. Furthermore, the agricultural harvester includes a computing system communicatively coupled to the sensor. The computing system is configured to identify the one or more subsurface soil layers within the field based on the data captured by the sensor and generate a tillage prescription map for use during a subsequent tillage operation based on the identified one or more subsurface soil layers. The tillage prescription map, in turn, prescribes a penetration depth for a tillage tool at a plurality of locations within the field.

Implementations are disclosed for automatic commissioning, configuring, calibrating, and/or coordinating sensor-equipped modular edge computing devices that are mountable on agricultural vehicles. In various implementations, neighbor modular edge computing device(s) that are mounted on a vehicle nearest a given modular edge computing device may be detected based on sensor signal(s) generated by contactless sensor(s) of the given modular edge computing device. Based on the detected neighbor modular edge computing device(s), an ordinal position of the given modular edge computing device may be determined relative to a plurality of modular edge computing devices mounted on the agricultural vehicle. Based on the sensor signal(s), distance(s) to the neighbor modular edge computing device(s) may be determined. Extrinsic parameters of the given modular edge computing device may be determined based on the ordinal position of the given modular edge computing device and the distance(s).

The invention relates to an adjustable hoeing device (10) for removing weeds situated on a ground surface, the hoeing device (10) having a supporting frame (50), on which there is arranged at least one hoeing unit (100), which hoeing unit comprises the following:—at least one main bar (200) and at least two transverse bars (250) arranged parallel to one another, which transverse bars (250) are each mounted rotatably on the at least one main bar (200);—at least two connection bars (300), which run parallel to the at least one main bar (200) and which mechanically connect the at least two transverse bars (250) to one another;—at least two blades (500), wherein the blades (500) are arranged on the at least two transverse bars (250) at a normal distance from the main bar (200); and—at least one actuation device (400), which acts on at least two transverse bars (250) or a transverse bar (250) and the main bar (200) in order to rotate same by means of a linearly displaceable actuation arm.

A computer-implemented method of controlling a mobile agricultural machine includes obtaining prior field data representing a position of plants in a field, obtaining in situ plant detection data from operation of the mobile agricultural machine in the field, determining a position error in the prior field data based on the in situ plant detection data, and generating a control signal that controls the mobile agricultural machine based on the determined position error.

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.

An apparatus includes at least one processor configured to obtain multiple spatiotemporal population projection models. Different spatiotemporal population projection models are associated with different pests, diseases, or biocontrol agents in a growing area. Each spatiotemporal population projection model defines how the associated pest, disease, or biocontrol agent spreads and contracts in the growing area over time. The at least one processor is also configured to receive information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more locations in the growing area. Different locations in the growing area are associated with different plants. The at least one processor is further configured to project a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent.

A modular system includes a hub and a set of modules removably coupled to the hub. The modules are physically coupled to the frame relative to each other so that each module can operate with respect to a different row of a field. An individual module includes a sensor for capturing field measurement data of individual plants along a row as the modular system moves through the geographic region. An individual module further includes a treatment mechanism for applying a treatment to the individual plants of the row based on the field measurement data before the modular system passes by the individual plants. An individual module further includes a computing device that determines the treatment based on the field measurement data and communicates data to the hub. The hub is communicatively coupled to the modules, so that it may exchange data between the modules and with a remote computing system.

Novel, universal, in-field/in-orchard, well-controlled, scalable, easy-to-use, and cost-effective management systems and methods for artificial pollination of agricultural areas such as orchards and fields are provided.

A system for automatically determining a trip magnitude of a ground engaging tool of an agricultural implement includes a ground-engaging system having an attachment structure coupled to a frame of an agricultural implement, a ground-engaging tool rotatably coupled to the attachment structure at a joint, and a biasing element configured to bias the ground-engaging tool towards a predetermined ground-engaging position. The system further includes a trip sensor configured to generate data indicative of a magnitude of rotation of the ground-engaging tool, the trip sensor being at least partially received within the biasing element. Additionally, the system includes a computing system communicatively coupled to the trip sensor, the computing system being configured to determine the magnitude of rotation of the ground-engaging tool based at least in part on the data generated by the trip sensor.

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, at least, detecting an optical sight to align with an associated agricultural object, tracking the agricultural objects relative to the optical sight, predicting a parameter to track in association with agricultural object, and activating an emitter to apply an action based on the parameter.