The present invention relates to the technical field of identification on adulterated meat, and in particular, to a method for identifying raw meat and high-quality fake meat based on a gradual linear array change of a component. The present invention spatially characterizes changing rules of featured components in the meat with the utilization of sensitivities of the visible/near-infrared spectral signals to changes of the components in the meat and the advantage that spectral scanning can acquire optical signals of the samples spatially and consecutively, further constructs the identification model according to differences in components and spectra of a region of interest in the hyperspectral image by taking a derivative for characterizing rates of change of the featured components.

A method includes steps of: capturing an image of a container; recognizing at least one object in the container based on the image; determining at least one first coordinate set corresponding to the at least one object; determining at least one second coordinate set that corresponds to target one (s) of the at least one first coordinate set and that relates to a fixed picking device of a robotic arm; adjusting position(s) of unfixed picking device(s) of the robotic arm if necessary; controlling the robotic arm to pick up one (s) of the at least one object that correspond(s) to the at least one second coordinate set with the fixed picking device and/or at least one unfixed picking device.

A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

The present invention relates to a method for analysing a set of food particles, comprising the steps of acquiring a digital colour image of a plurality of particles arranged on a substrate, detecting portions of the image corresponding to each particle, determining the colour of each portion of the determined image, and measuring a distance between the determined colour of one portion of the image and a predetermined colour of a predetermined particle type.

A process includes receiving a target quality value, receiving a measured quality value, receiving a source quality value, and sending a source control instruction. The source control instruction is based at least in part on the target quality value, the measured quality value, and the source quality value. The target quality value, the measured quality value, the source quality value, and the source control instruction are communicated via the communication port. The measured quality value is generated by an inspection device configured to inspect a sample. The source quality value is associated with a quality level of a first group of samples. The target quality value indicates a desired quality value of an output group of samples. The source control instruction causes a source selecting device to select one of a plurality of groups of samples, each group having identified quality characteristics.

An object recognition method of a refrigerator is disclosed. The disclosed object recognition method of a refrigerator comprises the steps of: obtaining a captured image of a storage compartment of a refrigerator; checking the change in the imaging direction of an image capturing device which has captured the image of the storage compartment, when a change in the captured image is confirmed compared to a previously stored image; and performing an object recognition operation of the captured image when the imaging direction is maintained.

Various examples of a system for peanut sorting and grading are disclosed herein. The system for grading peanut maturity, can include: a sample feeder configured to supply individual peanuts to an imaging area; a sorting board comprising a plurality of chutes and a plurality of gates, each chute of the plurality of chutes designated for a grade of peanut; and program instructions to obtain the digital image of the individual peanut; determine the grade of the individual peanut; and sort the individual peanut based on the grade of the individual peanut. A method for grading peanut maturity, can include feeding an individual peanut to an imaging area; obtaining a digital image of the individual peanut; determining a grade of the individual peanut based on an average color; and sorting the individual peanut in a chute of a sorting board based on the grade of the individual peanut.

A beverage dispenser includes a nozzle to dispense a beverage. The beverage dispenser further includes a camera to capture an image of the beverage as the beverage is dispensed from the nozzle. The camera has a field of view that includes the beverage. The beverage dispenser further includes a light source that illuminates the field of view of the camera. The beverage dispenser further includes a computer. The computer analyzes the image of the beverage and determines a characteristic of the beverage.

A method of automatically orienting symmetric and asymmetric food items, such as apples for example, is provided. Individual items of food are manipulated by a programmable manipulator within the view of one or more depth imaging cameras. Digital three dimensional characterizations of the surface of the food items are generated by the depth imaging camera or cameras and are utilized by a computer connected to the depth imaging camera or cameras to locate the stem and blossom of each food item. Asymmetric food items, such as apples with dropped shoulders as well as symmetric food items can be properly oriented and processed automatically.

Disclosed herein is a colony analysis apparatus, which cultures microorganisms collected from food subject to microbial collection on a plurality of Petri substrates and analyzes colors and the number of the cultured microorganisms by image recognition, thereby accurately determining and analyzing the contamination level of the food subject to microbial collection by an average value of the analysis.