Embodiments relate to a crop harvesting apparatus configured to garner or harvest crops from plants via vacuum suction and sort the garnered crops via a quick-switching gate system. A vacuum source generates the vacuum suction for the apparatus so that crops are garnered (or plucked) from the plant via suction through an end-effector, which are then transferred to a crop sorter by way of tubing that has a smooth inner surface. The crop sorter utilizes a gate system that exploits vacuum suction from the vacuum source and gravity to quickly and effectively sort the garnered crops into a hopper and a rejection bin.

Detecting a chewing noise from a user during a chewing session, triggering operation of a camera, obtaining image data capturing a food product, identifying the food product based on image data, determining a measurement of the chewing session, determining a volume of the food product based on the measurement of the chewing session, and determining a calorie intake based on the food product, the volume of the food product, and the measurement of the chewing session.

Detecting a chewing noise from a user during a chewing session, triggering operation of a camera, obtaining image data capturing a food product, identifying the food product based on image data, determining a measurement of the chewing session, determining a volume of the food product based on the measurement of the chewing session, and determining a calorie intake based on the food product, the volume of the food product, and the measurement of the chewing session.

The disclosure relates to a method for processing plants in a field in which a specific type of crop is planted, said method having the following steps: selecting a processing tool for processing plants; acquiring an image of the field, the image being correlated with position information; determining a position of a plant to be processed in the field using a neural network into which the acquired image is input, the neural network having a plurality of heads and in particular one of the heads being evaluated according to the processing tool and/or the type of crop grown; guiding the processing tool to the position of the plants; and processing the plants using the processing tool.

The disclosure relates to a method for processing plants in a field in which a specific type of crop is planted, said method having the following steps: selecting a processing tool for processing plants; acquiring an image of the field, the image being correlated with position information; determining a position of a plant to be processed in the field using a neural network into which the acquired image is input, the neural network having a plurality of heads and in particular one of the heads being evaluated according to the processing tool and/or the type of crop grown; guiding the processing tool to the position of the plants; and processing the plants using the processing tool.

Disclosed is a method for controlling appetite using smart glasses having camera and display functions, the method including: by an image processing apparatus, receiving an image captured by the camera from the smart glasses; determining whether a food image exists in the received image and extracting the food image; generating an overlay image in which the color of the extracted food image is converted; and transmitting the generated overlay image to the smart glasses so that the transmitted overlay image is overlaid on the image and displayed. According to the present disclosure, it is possible to control appetite using smart glasses to change the color of food included in the field of view.

Disclosed is a method for controlling appetite using smart glasses having camera and display functions, the method including: by an image processing apparatus, receiving an image captured by the camera from the smart glasses; determining whether a food image exists in the received image and extracting the food image; generating an overlay image in which the color of the extracted food image is converted; and transmitting the generated overlay image to the smart glasses so that the transmitted overlay image is overlaid on the image and displayed. According to the present disclosure, it is possible to control appetite using smart glasses to change the color of food included in the field of view.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image segmentation and chemical analysis using machine learning. In some implementations, a system obtains a hyperspectral image that includes a representation of an object. The system segments the hyperspectral image to identify regions of a particular type on the object. The system generates a set of feature values derived from image data for different wavelength bands that is located in the hyperspectral image in the identified regions of the particular type. The system generates a prediction of a level of one or more chemicals in the object based on an output produced by a machine learning model in response to the set of feature values being provided as input to the machine learning model. The system provides data indicating the prediction of the level of the one or more chemicals in the object.

An image display device according to disclosed one aspect comprises: a plate for detecting the weight of food ingredients; a projector for projecting images onto the plate; a camera for capturing food ingredients and user inputs relative to the images projected; and a controller for controlling the projector so that the changed images are outputted in response to the user inputs or changes in weight of the food ingredients.

An image display device according to disclosed one aspect comprises: a plate for detecting the weight of food ingredients; a projector for projecting images onto the plate; a camera for capturing food ingredients and user inputs relative to the images projected; and a controller for controlling the projector so that the changed images are outputted in response to the user inputs or changes in weight of the food ingredients.