An identification apparatus includes a plurality of collecting units configured to collect scattered light from a plurality of test items, a first spectroscopic unit configured to disperse light from part of the plurality of collecting units, a second spectroscopic unit configured to disperse light from a remaining part of the plurality of collecting units, an imaging unit configured to acquire a first spectrum projected from the first spectroscopic unit and a second spectrum projected from the second spectroscopic unit, the imaging unit including a plurality of light receiving elements arranged at least in a first direction, and an acquisition unit configured to acquire information about the test items based on an output signal from the imaging unit. One of a wavenumber of the first spectrum and a wavenumber of the second spectrum in the first direction increases while the other one decreases.

A system is provided for the identification and separation of heterogeneous material, the system comprising: a hyperspectral identification system for capturing spectra of material; a computer receiving and analyzing data from the hyperspectral identification system and selecting desired materials from the heterogeneous materials; and an ejection system, whereby the desired materials are ejected from the system.

A system is provided for the identification and separation of heterogeneous material, the system comprising: a hyperspectral identification system for capturing spectra of material; a computer receiving and analyzing data from the hyperspectral identification system and selecting desired materials from the heterogeneous materials; and an ejection system, whereby the desired materials are ejected from the system.

A system and method of inspecting a chicken piece comprising generating an image for the chicken piece, identifying a defect type, a defect location and an area of each defect on the chicken piece based on the image, and grading the chicken piece into one of a plurality of grades based on the defect type and the area.

A system and method of inspecting a chicken piece comprising generating an image for the chicken piece, identifying a defect type, a defect location and an area of each defect on the chicken piece based on the image, and grading the chicken piece into one of a plurality of grades based on the defect type and the area.

A system for categorizing seeds of plants into hybrid and non-hybrid categories. Seeds sorted according to the disclosed system are also disclosed.

The present disclosure provides methods and systems for autonomous data collection and system control of a material recovery facility (MRF). A control system receives data from set of sensors that reflect a status of the MRF. The control system determines an operating status of various MRF components (e.g., material handling units (MHUs), sensors, and/or other elements) and/or a composition of a waste stream being processed by the MRF. The control system controls MHU(s) based on the data to optimize the recovery and/or purity of recoverable materials from the waste stream. The control system may rearranging MHUs within the MRF and/or re-tasking individual MHUs to perform different handling functions. Machine learning (ML) and/or artificial intelligence (AI) mechanisms can be used to optimize the operation of the MRF in an autonomous fashion. Other aspects are also disclosed.

The present disclosure provides methods and systems for autonomous data collection and system control of a material recovery facility (MRF). A control system receives data from set of sensors that reflect a status of the MRF. The control system determines an operating status of various MRF components (e.g., material handling units (MHUs), sensors, and/or other elements) and/or a composition of a waste stream being processed by the MRF. The control system controls MHU(s) based on the data to optimize the recovery and/or purity of recoverable materials from the waste stream. The control system may rearranging MHUs within the MRF and/or re-tasking individual MHUs to perform different handling functions. Machine learning (ML) and/or artificial intelligence (AI) mechanisms can be used to optimize the operation of the MRF in an autonomous fashion. Other aspects are also disclosed.

A distribution system for use in an induction system with an object processing system. The distribution system provides dissimilar objects into one of a plurality of receiving units. The distribution system includes an air intake system with an opening that is a fixed distance from a conveyor section, said air intake system aiding in moving an object on the conveyor section from the conveyor section to one of a plurality of adjacent transport units.

An optical sorter includes a light source, a color sensor, a determination part configured to determine a foreign object and/or a defective product, a color shift amount calculation part configured to calculate, based on a color image acquired by the color sensor, an amount of a color shift that occurs due to the fact that at least two element groups out of an R element group, a G element group, and a B element group are spaced apart from each other in a transit direction of a sorting target, a speed calculation part configured to calculate a transit speed of the sorting target based on a separation distance between the at least two element groups in the transit direction, the color shift amount, and a scan time, which is a time required for the color sensor to scan the sorting target once, and a sorting device configured to perform a trajectory change operation for changing a trajectory of a specific sorting target determined based on a result of the determination by the determination part at a timing determined based on the transit speed calculated by the speed calculation part.