Abstract: In variants, the method can include: sampling cavity measurements of the cook cavity; determining an image representation using the cavity measurements; determining a food class based on the image representation; optionally comparing the image representation to prior image representations; optionally determining a new food class based on the image representation; optionally updating a food identification module with the new food class; and optionally determining a cook program associated with the new food class.

Abstract: The system is preferably used with a cooking appliance with a heating cavity and can include: a vessel and a temperature probe. The system can optionally include: a lid, a tray, and/or a circulator. However, the system can include any other suitable components. The system functions to monitor and/or control the temperature of a volume of working fluid in a cooking appliance with a heated cavity.

Abstract: In variants, the method can include: sampling cavity measurements of the cook cavity; determining an image representation using the cavity measurements; determining a food class based on the image representation; optionally comparing the image representation to prior image representations; optionally determining a new food class based on the image representation; optionally updating a food identification module with the new food class; and optionally determining a cook program associated with the new food class.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: In variants, the system can include an extensible cooking system (e.g., grill) with a removable user interface configured to control cooking system operation and/or a set of removable accessory modules. Examples of removable accessory modules can include user fuel control interfaces and sensor ports.

Abstract: In variants, an automatically-identifiable temperature probe can include: a probe body, one or more sensors, a connector, and/or any other suitable components. In variants, the method for temperature determination can include: determining a set of electrical signals, determining a temperature probe type based on the set of electrical signals, determining a sensor resolution model based on the temperature probe type, and determining a set of final temperature estimates based on the set of electrical signals and the sensor resolution model.

Abstract: In variants, the method can include: sampling cavity measurements of the cook cavity; determining an image representation using the cavity measurements; determining a food class based on the image representation; optionally comparing the image representation to prior image representations; optionally determining a new food class based on the image representation; optionally updating a food identification module with the new food class; and optionally determining a cook program associated with the new food class.

Abstract: In variants, a method for individual heating element control can include: identifying food within a cook cavity of the cooking appliance, determining a set of cooking instructions associated with the food, the cooking instructions including a different model for each heating element subset, dynamically determining control instructions for each heating element subset based on the respective model, and controlling each heating element subset based on the respective control instructions.

Abstract: A connected oven, including a set of in-cavity sensors and a processor configured to automatically identify foodstuff within the cooking cavity, based on the sensor measurements; and automatically operate the heating element based on the foodstuff identity.

Abstract: A connected oven, including a set of in-cavity sensors and a processor configured to automatically identify foodstuff within the cooking cavity, based on the sensor measurements; and automatically operate the heating element based on the foodstuff identity.

Abstract: In variants, a method for individual heating element control can include: identifying food within a cook cavity of the cooking appliance, determining a set of cooking instructions associated with the food, the cooking instructions including a different model for each heating element subset, dynamically determining control instructions for each heating element subset based on the respective model, and controlling each heating element subset based on the respective control instructions.

Abstract: A cooking system, including: a cooking accessory, optionally one or more auxiliary components, optionally one or more external systems, and/or any other suitable components. The cooking accessory can include: a housing, a temperature probe, a communication module, a power source, a user interface, one or more connectors, optionally a motion sensor, optionally an external retention mechanism, a processing system, and/or any other suitable components.

Abstract: A connected oven, including a set of in-cavity sensors and a processor configured to automatically identify foodstuff within the cooking cavity, based on the sensor measurements; and automatically operate the heating element based on the foodstuff identity.

Abstract: An imaging system for a high-heat cooking appliance, including a camera system including an optical sensor, a lens, and a set of light emitters; and a camera cooling mechanism thermally connected to the camera system.

Abstract: The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.