This work map provision server includes: a storage unit that has stored therein a plurality of work maps which individually comprise work setting information of respective small regions in a farm field and attribute information of the work maps; and a map selection unit that, upon receipt of a map acquisition request from a work machine, selects, from among the plurality of maps, one or more work maps suited to said map acquisition request on the basis of map narrowing-down information contained in said map acquisition request and/or reception time and date of said map acquisition request and also on the basis of the attribute information of the respective work maps, and then transmits the selected work maps to the work machine.

The invention relates to a method and forest work machine (1) making use of it, by means of which the control of the forest work machine is assisted. In the forest work machine, a light source (2) is adapted, which may be directed to one or more different parts of the surroundings around it, forming the working area of the forest work machine. This is adapted to produce at least one specifically defined specified-form light pattern (3) on said working area. With such a light pattern, visual information content may be produced on the working area to support an operator of the forest work machine in their work. As content of such information, it is possible to define control measures for the forest work machine, performed in any working area or their restrictions.

An image acquisition device and image processing method for an agricultural harvesting operation machine. The agricultural harvesting operation machine comprises a body and an image acquisition device; the image acquisition device comprises an image capturing component and a communication machine; the image acquisition device is mounted on the body; the communication machine is communicatably connected to the image capturing component so as to obtain acquired image information from the image capturing component, thereby assisting in adjusting the working position and operation speed of the body. Also provided is an image processing method. Information required by harvesting operation is taken into full consideration, images of the position of an agricultural harvesting operation machine are acquired, and multiple acquired images are efficiently processed.

An illustrative control system for a precision agricultural implement includes a controller having a convolutional neural network, an imaging device, a plurality of sensors, and a plurality of actuators in communication with the controller, the controller configured for detecting and tracking objects of interest in a commodity field, such a commodity plants, and the plurality of actuators including a plurality of agricultural tool actuators the controller operates based on the detection and tracking of object of interest in the commodity field.

A robotic automated broccoli plant harvesting apparatus uses a vision system implemented with an RGB depth camera that provides imaging signals. Software operating on a microcomputer processes the imaging signals to identify the height and position of a broccoli plant to sever it with dual opposed cutting blades. In a preferred embodiment, the software algorithm combines color, texture, and distance information of the broccoli plant to reliably cut the broccoli stalk with ⅛-inch (3.175-millimeter) accuracy. Broccoli stalks of consistent lengths result in uniform, commercially viable products.

An object-collection system is disclosed. The system including a vehicle connected to a bucket, a camera connected to the vehicle, and an object picking assembly configured to pick up objects off of ground. The system further includes a processor that obtains object information for identified objects, guides the object-collection system over a target geographical area toward the identified objects based on the object information, captures images of the ground relative to the object picker as the object-collection system is guided towards the identified objects, identifies a target object in the images, tracks movement of the target object across the images as the object-collection system is guided towards the identified objects, and employs the tracked movement of the target object to instruct the object picker to pick up the target object.

An apparatus for controlling weeds, which includes a tine formation adapted to remove or disrupt targeted weeds, and a tine support assembly adapted to support the tine for movement about a first control axis in a generally vertical direction between an engaged position wherein the tine formation in use contacts a ground surface for removal or disruption of targeted weeds and a disengaged position wherein the tine formation is substantially retracted from the ground surface. The tine support assembly is further adapted to support the tine for movement about a second control axis in a generally horizontal direction. The tine support assembly further includes a first actuation mechanism adapted to effect movement of the tine about the first control axis, and a second actuation mechanism adapted to effect movement of the tine about the second control axis. The apparatus also includes a sensing system for sensing aspects of an environment and generating data indicative thereof, and a classification system for identifying target weeds within the environment on the basis of the data from the sensing system. A control system is adapted to activate the first and second actuation mechanisms of the tine support assembly in accordance with a predetermined control logic thereby sequentially to position the tine for disruptive contact with the targeted weeds.

A control system uses visual odometry (VO) data to identify a position of the vehicle while moving along a path next to the row and to detect the vehicle reaching an end of the row. The control system can also use the VO image to turn the vehicle around from a first position at the end of the row to a second position at a start of another row. The control system may detect an end of row based on 3-D image data, VO data, and GNSS data. The control system also may adjust the VO data so the end of row detected from the VO data corresponds with the end of row location identified with the GNSS data.

An unmanned aerial vehicle for application of an active ingredient to agricultural crops is configured to hold: a liquid comprising the active ingredient in at least one liquid reservoir housed within or attached to the unmanned aerial vehicle. At least one liquid application unit is housed within or attached to the unmanned aerial vehicle and in fluid communication with the at least one liquid reservoir. The housing of at least one sensor unit which is configured to determine sense information comprising sensing a functioning of one or more of: the at least one liquid reservoir, the at least one liquid application unit, the at least one operating unit. A processing unit determines an instruction to the unmanned aerial vehicle to dock with a base station and get a service from the base station. This determination step comprises utilization of sense information.

A system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.