A system classifies materials utilizing a vision system that implements an artificial intelligence system in order to identify or classify and then remove automotive airbag modules from a scrap stream, which may have been produced from a shredding of end-of-life vehicles. The sorting process may be designed so that live airbag modules are not activated, which may cause damage to equipment or persons.

A medicine sorting device includes: a discrimination part for discriminating a type of a medicine based on an image captured by a first camera; and a conveyance/sorting unit for sorting, by each type, a plurality of types of medicines accommodated in a mixed state in a first accommodating part based on a discrimination result of the discrimination part, and storing the medicines in a second accommodating part.

Systems for sorting seeds are disclosed, as well as batches of seeds that have been sorted using the systems.

A system for sorting seeds based on their resistance to a stress is disclosed. Batches of purified seeds sorted using the system are also disclosed.

Embodiments described herein relate to hardware and software for waste item detection and recognition, along with an education or feedback system. Embodiments described herein use artificial intelligence, which embodies machine learning and computer vision, to detect waste items and generate feedback to nudge the user to dispose the waste items into appropriate receptacles while generating smart operational insights of a designated premise.

Embodiments described herein relate to hardware and software for waste item detection and recognition, along with an education or feedback system. Embodiments described herein use artificial intelligence, which embodies machine learning and computer vision, to detect waste items and generate feedback to nudge the user to dispose the waste items into appropriate receptacles while generating smart operational insights of a designated premise.

An inspection unit for performing an optical inspection on a grain to be transferred by transfer means includes a visible light source, a near-infrared light source, a visible light detection unit, and a near-infrared light detection unit. A determination unit plots wavelength components of red (R), green (G), and blue (B), and a near-infrared light component in a three-dimensional space, to create a three-dimensional optical correlation diagram, for a plurality of good product samples and defective product samples, and sets a threshold value, in which: the wavelength components are detected by the visible light detection unit; and the near-infrared light component is detected by the near-infrared light detection unit.

An inspection unit for performing an optical inspection on a grain to be transferred by transfer means includes a visible light source, a near-infrared light source, a visible light detection unit, and a near-infrared light detection unit. A determination unit plots wavelength components of red (R), green (G), and blue (B), and a near-infrared light component in a three-dimensional space, to create a three-dimensional optical correlation diagram, for a plurality of good product samples and defective product samples, and sets a threshold value, in which: the wavelength components are detected by the visible light detection unit; and the near-infrared light component is detected by the near-infrared light detection unit.

A process includes receiving a target quality value, receiving a measured quality value, and sending an inspection control instruction. The inspection control instruction is based at least in part on the target quality value and the measured quality value. The measured quality value is generated by a product inspector configured to inspect a sample before being sorted and may also include data generated by an output product inspector configured to inspect a sample after passing a sorting phase. The target quality value indicates a desired quality value of an output group of samples. The inspection control instruction controls the method of inspection performed by the product inspector. The measured quality values are received by a computing device, which in turn outputs the inspection control instruction to a product inspector.

In an embodiment, a method may determine a first candidate item type and a second candidate item type for a container. The method may determine appearance feature(s) of the first candidate item type and appearance feature(s) of the second candidate item type. The method may determine, using a trained dissimilarity model, a distinguishability score between the first candidate item type and the second candidate item type based on the appearance feature(s) of the first candidate item type and the appearance feature(s) of the second candidate item type. The method may determine to store the first candidate item type and the second candidate item type together in the container based on the distinguishability score between the first candidate item type and the second candidate item type. In some instances, the method may place item(s) of the first candidate item type and item(s) of the second candidate item type in the container.