A method of estimating weight of food objects, includes training an artificial neural network software module, and using the trained artificial neural network software module to provide a weight correlated data estimate for said food object based on a three-dimensional image of the food object

A method and system are provided for automatically portioning workpieces, such as food products, by simulating portioning the workpieces in accordance with the one or more desired shapes of the final piece(s) as a directly controlled physical characteristic (parameter/specification) as well as one or more resulting indirectly controlled physical characteristics (parameters/specifications). The desired shape(s) of the final piece(s) are defined by a plurality of manipulatable reference coordinates. A workpiece is scanned to obtain scanning information, then portioning of the workpiece is simulated in accordance with the desired shape(s) of the final piece(s) defined by the directly controlled reference coordinates, thereby to determine the one or more indirectly controlled physical characteristics of the one or more final pieces to be portioned from the workpiece. The simulated portioning of the workpiece is performed for multiple combinations of directly controlled shapes as defined by the modified or edited reference coordinates and indirectly controlled physical characteristics until an acceptable set of a directly controlled shape and resulting one or more indirectly controlled physical characteristics is determined.

A method and a system provide a feedback data indicating quality of a food processing performed by an operator. The method and system involve acquiring at least one image data of a food product from an operator; and processing the acquired image data. The processing includes detecting whether undesired objects are present in the food product, obtaining, in case undesired objects are detected, position data of the undesired objects within the food product, and utilizing the position data in issuing a feedback indicator indicating a position where the detected undesired objects are present within the food product processed by the operator.

The present disclosure involves an intelligent method and device for cutting squid white slices, the intelligent method being implemented on the intelligent device for cutting squid white slices. The method may be implemented on a calculating device, the calculating device may have at least one processor and at least one storage medium including an instruction set used for cutting the squid white slices, the method including: reading a laser point cloud data of a three-dimensional (3D) topography of the squid white slices; optimizing the laser point cloud data; extracting an effective area of the squid white slices; determining a cutting zero point; determining a cutting process area; and determining optimization of a cutting point position and a cutting angle. The one or more embodiments provided by the present disclosure may satisfy the needs of large-scale continuous production in factories, reduce labor costs, and improve production efficiency.

The present disclosure involves a calculation method and device for intelligent cutting squid white slices, the calculation method being implemented on the device for intelligent cutting squid white slices. The calculation method may be implemented on a calculation device, the calculation device may have at least one processor and at least one storage medium including an instruction set used for intelligent cutting the squid white slices, the calculation method including: reading a laser point cloud data of a three-dimensional (3D) topography of the squid white slices; optimizing the laser point cloud data; extracting an effective area of the squid white slices; determining a cutting zero point; determining a cutting process area; and determining optimization of a cutting point position and a cutting angle. The one or more embodiments provided by the present disclosure may satisfy the needs of large-scale continuous production in factories, reduce labor costs, and improve production efficiency.

A cutting assembly and corresponding workpiece processing for cutting a workpiece, the cutting assembly including a first cutting tool adapted to cut the workpiece along a first cutting plane, and a second cutting tool adapted to cut the workpiece along a second cutting plane, the first and second cutting planes defining an inner angle therebetween, the cutting assembly being adapted to cut the workpiece along the first and second cutting planes in a single operation.

In an embodiment, an apparatus for processing meat product and a method of using same is provided. A first station includes a flattener which is configured to flatten a meat product, and a scanner which is configured to scan features of the meat product after flattening. A second station is configured to dock front and rear ends of the meat product in accordance with information received from the scan of the features.

A learning device includes an input processor and a learning processor. The input processor acquires a physical quantity related to a cutting process, and inputs a state variable based on the physical quantity to the learning processor, and the learning processor updates, based on a measured cutting result, an evaluation model that outputs an evaluation result of the cutting process based on the state variable.

A food processing system and method for cutting a food item into cut food item portions includes a conveyor, an imaging system, a cutting device, and a controller. The conveyor conveys the food item past the imaging system allowing the imaging system to image the food item and image data is obtained. The controller receives the imaging data and determines the location of defective areas on the basis thereof. Then a distribution of portion cuts over the food item dividing the food item into food item portions is determined on the basis of at least the determined location of defective areas and a desired portion size and/or weight. The cutting device cuts the food item according to the determined distribution of portion cuts.

The present disclosure involves a calculation method and device for intelligent cutting squid white slices, the calculation method being implemented on the device for intelligent cutting squid white slices. The calculation method may be implemented on a calculation device, the calculation device may have at least one processor and at least one storage medium including an instruction set used for intelligent cutting the squid white slices, the calculation method including: reading a laser point cloud data of a three-dimensional (3D) topography of the squid white slices; optimizing the laser point cloud data; extracting an effective area of the squid white slices; determining a cutting zero point; determining a cutting process area; and determining optimization of a cutting point position and a cutting angle. The one or more embodiments provided by the present disclosure may satisfy the needs of large-scale continuous production in factories, reduce labor costs, and improve production efficiency.