Abstract: A method of identifying a level of doneness of food includes: obtaining an initial food detection image; obtaining a current food detection image; performing a differential fusion operation on the initial food detection image and the current food detection image to obtain a fused food detection image; and inputting the fused food detection image into a predetermined neural network model to obtain the level of doneness of food output by the predetermined neural network model. According to the method, the differential image fusion is performed to reduce an influence on determining the level of doneness of food caused by the initial food detection image, and costs of the device may be reduced.

Abstract: A method of identifying a level of doneness of food includes: obtaining an initial food detection image; obtaining a current food detection image; performing a differential fusion operation on the initial food detection image and the current food detection image to obtain a fused food detection image; and inputting the fused food detection image into a predetermined neural network model to obtain the level of doneness of food output by the predetermined neural network model. According to the method, the differential image fusion is performed to reduce an influence on determining the level of doneness of food caused by the initial food detection image, and costs of the device may be reduced.

Abstract: A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.

Abstract: A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.

Abstract: A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.

Abstract: A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.