A computer-implemented method includes performing, using a processor onboard a vehicle, a machine learning (ML) processing on sensor input from sensors onboard an agricultural vehicle, identifying, according to a rule, a subset of data resulting from the ML processing and generating and displaying, in real-time, the subset of data to a user interface, thereby enabling a user interaction with the subset of data.

A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor includes receiving sensor input from the sensor; processing the sensor input by multiple machine learning (ML) algorithms, each using a corresponding ML model for generating labels for objects identified in the sensor input; combining labels generated by each ML algorithm to generate a super-imposed labeled sensor input frame; comparing outputs of the ML algorithms to determine similarities or differences; and using results of the comparing for improving an operational characteristic of the sensor input processing.

A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor, includes capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment is applied to the target object; processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of a treatment action in the vicinity of the target object; and providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.

A method of processing agricultural images includes comparing object detections performed by multiple image processing schemes to determine a set of ground truth images from which at least one machine learning (ML) models used by at least one ML algorithm included in the multiple image processing schemes is trained, wherein the multiple image processing schemes include two or more of (a) an image processing scheme that includes a cascade of multiple ML algorithms; (b) an image processing scheme that includes image annotation based on user feedback; or (c) an image processing scheme that includes a cascade of an ML algorithm or a computer vision (CV) algorithm and a user feedback.

A gamified application is provided for users to feed animated virtual characters with images of real-world food items. The images fed to the virtual characters are to be uploaded to a data store in a cloud environment, for use in training a custom machine learning model. A server in the cloud environment receives a photo of a food item fed to a virtual character in an augmented reality environment in the gamified application executing on a user device, invokes the custom machine learning model to generate classification information for the photo, sends the classification information to the user device for verification by a user, and stores the verified information to the data store used for periodically training the machine learning model. Over time, the data store would include a large volume of food images with label data verified by a large number of users.

A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.

System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.

Systems for self-organizing data collection in an industrial environment are disclosed. An example system may include a self-propelled mobile data collector for handling a plurality of sensor inputs from sensors in the industrial environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may include a self-organizing system for self-organizing at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system organizes a swarm of self-propelled mobile data collectors to collect data from a plurality of target systems in the industrial environment.

Systems for self-organizing data collection and storage in a power generation environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the power generation system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

Systems and methods for data collection in an industrial production system including a plurality of components are disclosed. An example system may include a sensor communication circuit structured to interpret a plurality of data values from a sensed parameter group, the sensed parameter group including a plurality of sensors including a vibration sensor and a temperature sensor, and the plurality of sensors operatively coupled to at least one of the plurality of components; a data analysis circuit structured to detect an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and a response circuit structured to modify a production-related operating parameter of the industrial production system in response to the detected operating condition.