Abstract: Embodiments include technologies that use machine learning to control mobile machines as well as to adjust settings of mobile machines in real time (e.g., agricultural machine settings, construction machine settings, forestry machine settings, or landscaping machine settings). Some embodiments include a method that includes using a mobile machine to perform work in a field using first machine settings and recording performance information. The performance information indicating a performance of the mobile machine while performing the work in the field. Also, the method includes using a computing system to input the performance information into a trained deep learning model and to receive new machine settings information from the trained model, and using the computing system to control the machine according to the new settings information.

Abstract: Embodiments include technologies that use machine learning to update a route plan to re-route a mobile machine in a field. In some examples, a method includes using a mobile machine to perform work in a field and recording travelled path information. The recorded path information includes information about one or more paths followed by the machine when performing the work in the field. The method also includes using one or more computing devices on the machine to input the path information into a trained deep learning model and to receive new machine travel paths from the trained model. Also, the method includes using the computing device(s) on the mobile machine to control the machine to follow the new travel paths generated by the trained model.

Abstract: Embodiments include technologies that use machine learning to enhance machine settings (e.g., agricultural machine settings, construction machine settings, forestry machine settings, or landscaping machine settings). Some embodiments include a method that includes using machine learning to generate or update machine settings. In some examples, the method includes receiving, by a computing system, initial settings information, the initial settings information including settings used by or to be used by one or more mobile machines performing one or more tasks. The mobile machine(s) can include machines for farming, construction, forestry, or landscaping. In such examples, the method also includes training, by the computing system, a deep learning model using the settings information. Also, in such examples, the method includes using, by the computing system, the trained model to generate new settings information for a given task.

Abstract: Embodiments include technologies that use machine learning to plan a route. Some embodiments include a method that includes using machine learning to generate or update a route plan. In some examples, the method includes receiving, by a computing system, initial routing information, the initial routing information defining routes followed by or to be followed by one or more mobile machines in one or more fields. In such examples, the method also includes training, by the computing system, a deep learning model using the initial routing information. Also, in such examples, the method includes using, by the computing system, the trained model to generate new routing information for a given field.

Abstract: Provided herein is a dispensing nozzle cover with features including an elongated chamber having a proximal end and a distal end, where the proximal end is adapted to receive a dispensing nozzle, and the distal end is closed, a seal arranged in the vicinity of the proximal receiving end of the elongated chamber, the seal being arranged to seal against the dispensing nozzle, and an adhesive surface area facing the ambient atmosphere.

Abstract: A dispensing nozzle cover comprising an elongated chamber having a proximal end and a distal end, where the proximal end is adapted to receive a dispensing nozzle, and the distal end is closed, a seal arranged in the vicinity of the proximal receiving end of the elongated chamber, the seal being arranged to seal against the dispensing nozzle, wherein the dispensing nozzle cover further comprises an adhesive surface area facing the ambient atmosphere.

Abstract: A new and distinct cultivar of Begonia plant named ‘Kenna’ characterized by having larger area of red color on the outer edge of the petals and an inner petal that has deeper yellow color as compared to ‘Jutta’ grown under the same environmental conditions.