Generating hyperspectral image database by machine learning and mapping of color images to hyperspectral domain

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to:

• determine a geographical location of the user;
• determine availability of local food at the geographical location;
• receive as input a color image of the local food;
• convert the color image of the local food into a hyperspectral image;
• execute a machine learning model with the hyperspectral image of the local food as input to the machine learning model, the machine learning model classifying the hyperspectral image of the local food into features comprising at least taste, nutrient content, and chemical composition, the machine learning model having been trained to classify input comprising hyperspectral images into classification features comprising at least taste, nutrient content, and chemical composition;
• determine whether the features associated with the local food satisfy the food a consumption pattern associated with the user specified in a database;
• and
• responsive to determining that the features associated with the local food satisfy the food consumption pattern associated with the user specified in the database, recommend the local food, the device further caused to:
• receive input comprising at least color images of food a user consumes, text information associated with the food and audio information associated with the food;
• convert the color images into hyperspectral images;
• generate output classification comprising at least taste, nutrient content and chemical composition features associated with the food the user consumes, by the machine learning model learned to classify input comprising the hyperspectral images;
• and
• create the database of food consumption pattern associated with the user based on the output classification features associated with the hyperspectral images, the text information and the audio information.

A system comprising:

• at least one hardware processor;
• a memory device coupled to the at least one hardware processor;
• the at least one hardware processor operable to at least:
• receive input comprising at least color images of food a user consumes, text information associated with the food and audio information associated with the food;
• convert the color images into hyperspectral images;
• generate output classification comprising at least taste, nutrient content and chemical composition features associated with the food the user consumes, by a machine learning model learned to classify input comprising the hyperspectral images;
• create a database of food consumption pattern associated with the user based on the output classification features associated with the hyperspectral images, the text information and the audio information;
• determine a geographical location of the user;
• determine availability of local food at the geographical location;
• receive as input a color image of the local food;
• convert the color image of the local food into a hyperspectral image;
• execute the machine learning model with the hyperspectral image of the local food as input, the machine learning model classifying the hyperspectral image of the local food into features comprising at least taste, nutrient content, and chemical composition;
• determine whether the features associated with the local food satisfy the food consumption pattern associated with the user specified in the database;
• and
• responsive to determining that the features associated with the local food satisfy the food consumption pattern associated with the user specified in the database, recommend the local food.