System and method for hyperspectral image processing to identify object

1. A system comprising:

a memory; and

at least one processor to:

generate a synthetic training set comprising a plurality of hyperspectral images, the plurality of hyperspectral images based on actual hyperspectral images of an object, synthetic hyperspectral images of the object that include data added to the actual hyperspectral images of the object, and synthetic hyperspectral images of the object rendered in one of a different position and orientation;

acquire a hyperspectral image of the object by an imaging device, the hyperspectral image of the object comprising a three-dimensional set of images of the object, each image in the set of images representing the object in a wavelength range of the electromagnetic spectrum;

normalize the hyperspectral image of the object;

select a region of interest in the hyperspectral image, the region of interest comprising a subset of at least one image in the set of images;

extract spectral features from the region of interest in the hyperspectral image;

compare the spectral features from the region of interest with the plurality of hyperspectral images in the synthetic training set to determine particular characteristics of the object and determine a value for at least one quality parameter for the object; and

identify the object based on the spectral features.

2. The system of claim 1 , the at least one processor further to train an object model using data comprising the plurality of hyperspectral images in the synthetic training set and ground truth measurements associated with the object and determine the particular characteristics of the object based on the object model.

3. The system of claim 1 , the at least one processor further to determine dry matter content of the object, firmness of the object, and sugar content of the object.

4. The system of claim 1 , wherein the imaging device comprises a hyperspectral imaging device and the system further comprises at least one illumination device that illuminates the object and a conveyor belt that moves the object past the imaging device as the hyperspectral imaging device captures the hyperspectral image.

5. The system of claim 1 , wherein the object comprises a perishable food item.

6. The system of claim 5 , wherein the perishable food item comprises an avocado.

7. The system of claim 1 , wherein the object comprises an avocado and the at least one processor further to determine at least one of dry matter content of the avocado, firmness of the avocado, and sugar content of the avocado, based on the spectral features from the region of interest.

8. A method comprising:

generating, by a processor, a synthetic training set comprising a plurality of hyperspectral images, the plurality of images based on actual hyperspectral images of an object, synthetic hyperspectral images of the object that include data added to the actual hyperspectral images of the object, and synthetic hyperspectral images of the object rendered in one of a different position and orientation;

acquiring, by the processor, a hyperspectral image of the object by an imaging device, the hyperspectral image of the object comprising a three-dimensional set of images of the object, each image in the set of images representing the object in a wavelength range of the electromagnetic spectrum;

normalizing, by the processor, the hyperspectral image of the object;

selecting, by the processor, a region of interest in the hyperspectral image, the region of interest comprising a subset of at least one image in the set of images;

extracting, by the processor, spectral features from the region of interest in the hyperspectral image;

comparing, by the processor, the spectral features from the region of interest with the plurality of hyperspectral images in the synthetic training set to determine particular characteristics of the object and determining a value for at least one quality parameter for the object; and

identifying, by the processor, the object based on the spectral features.

9. The method of claim 8 , further comprising training an object model using data comprising the plurality of hyperspectral images in the synthetic training set and ground truth measurements associated with the object and determining the particular characteristics of the object based on the object model.

10. The method of claim 8 , further comprising determining dry matter content of the object, firmness of the object, and sugar content of the object.

11. The method of claim 8 , the imaging device comprising a hyperspectral imaging device and the method further comprising illuminating the object by at least one illumination device and moving the object past the imaging device by a conveyor belt as the hyperspectral imaging device captures the hyperspectral image.

12. The method of claim 8 , wherein the object comprises a perishable food item.

13. The method of claim 12 , wherein the perishable food item comprises an avocado.

14. The method of claim 8 , wherein the object comprises an avocado and the method further comprising determining at least one of dry matter content of the avocado, firmness of the avocado, and sugar content of the avocado, based on the spectral features from the region of interest.

15. A non-transitory computer-readable storage medium, having instructions stored thereon that, when executed by a computing device cause the computing device to perform operations, the operations comprising:

generating a synthetic training set comprising a plurality of hyperspectral images, the plurality of images based on actual hyperspectral images of an object, synthetic hyperspectral images of the object that include data added to the actual hyperspectral images of the object, and synthetic hyperspectral images of the object rendered in one of a different position and orientation;

acquiring a hyperspectral image of the object by an imaging device, the hyperspectral image of the object comprising a three-dimensional set of images of the object, each image in the set of images representing the object in a wavelength range of the electromagnetic spectrum;

normalizing the hyperspectral image of the object;

selecting a region of interest in the hyperspectral image, the region of interest comprising a subset of at least one image in the set of images;

extracting spectral features from the region of interest in the hyperspectral image;

comparing the spectral features from the region of interest with the plurality of hyperspectral images in the synthetic training set to determine particular characteristics of the object and determining a value for at least one quality parameter for the object; and

identifying the object based on the spectral features.

16. The non-transitory computer-readable storage medium of claim 15 , the operations further comprising training an object model using data comprising the plurality of hyperspectral images in the synthetic training set and ground truth measurements associated with the object and determining the particular characteristics of the object based on the object model.

17. The non-transitory computer-readable storage medium of claim 15 , the operations further comprising determining dry matter content of the object, firmness of the object, and sugar content of the object.

18. The non-transitory computer-readable storage medium of claim 15 , the imaging device comprising a hyperspectral imaging device and the operations further comprising illuminating the object by at least one illumination device and moving the object past the imaging device by a conveyor belt as the hyperspectral imaging device captures the hyperspectral image.

19. The non-transitory computer-readable storage medium of claim 15 , wherein the object comprises a perishable food item.

20. The non-transitory computer-readable storage medium of claim 19 , wherein the perishable food item comprises an avocado.