Managing inventory of perishable products

1. A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified perishable products, the system comprising:

at least one processor configured to:

receive a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store;

analyze the set of images to determine information about a displayed inventory of the plurality of perishable products, wherein analyzing the set of images includes comparing digital image data associated with the set of images to a product quality profile pattern, the product quality profile pattern having been generated by a machine learning model, the machine learning model having been trained using combinations of digital hyperspectral images and corresponding levels of product qualities including measured levels of hyperspectral wavelength information or waveband information;

obtain information about additional perishable products scheduled to be displayed on the at least one shelving unit;

use the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and

provide the at least one suggestion to an entity associated with the retail store.

2. The system of claim 1 , wherein the information about the displayed inventory includes quantity indicators for multiple types of perishable products, and wherein the at least one processor is configured to:

determine the quantity indicators for the multiple types of perishable products based on analysis of the set of images; and

use the determined quantity indicators for the multiple types of perishable products to determine the at least one suggestion regarding placement of perishable products in the retail store.

3. The system of claim 1 , wherein the information about the displayed inventory includes quality indicators for multiple types of perishable products, and wherein the at least one processor is configured to:

determine the quality indicators for the multiple types of perishable products based on analysis of the set of images; and

use the determined quality indicators for the multiple types of perishable products to determine the at least one suggestion regarding placement of perishable products in the retail store.

4. The system of claim 1 , wherein the information about the displayed inventory includes demand indicators for multiple types of perishable products, and wherein the at least one processor is configured to:

determine the demand indicators for the multiple types of perishable products based on analysis of the set of images; and

use the determined demand indicators for the multiple types of perishable products to determine the at least one suggestion regarding placement of perishable products in the retail store.

5. The system of claim 1 , wherein the obtained information about the additional perishable products includes a quantity indicator for the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the quantity indicator of the additional perishable products.

6. The system of claim 1 , wherein the obtained information about the additional perishable products includes a quality indicator for the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the quality indicator for the additional perishable products.

7. The system of claim 1 , wherein the obtained information about the additional perishable products includes a shelf-life estimation of the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the shelf-life estimation of the additional perishable products.

8. The system of claim 1 , wherein the obtained information about the additional perishable products includes costs associated with the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the costs of the additional perishable products.

9. The system of claim 1 , wherein the obtained information about the additional perishable products includes estimated arrival time of the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the estimated arrival time of the additional perishable products.

10. The system of claim 1 , wherein the obtained information about the additional perishable products includes a predicted demand for the additional perishable products, and wherein the at least one processor is configured to determine the at least one suggestion regarding placement of the additional perishable products based on the predicted demand for the additional perishable products.

11. The system of claim 1 , wherein the at least one suggestion includes an indication of a temporary location for at least some of the plurality of perishable products to reside prior to arrival of the additional perishable products, and wherein the at least one processor is configured to determine the temporary location for the at least some of the plurality of perishable products based on the information about the displayed inventory and the information about the additional perishable products.

12. The system of claim 1 , wherein the at least one placement suggestion includes an indication of a temporary size of a display area for at least some of the plurality of perishable products to be used prior to arrival of the additional perishable products, and wherein the at least one processor is configured to determine the temporary size based on the information about the displayed inventory and the information about the additional perishable products.

13. The system of claim 1 , wherein the at least one placement suggestion includes an indication of a temporary price for at least some of the plurality of perishable products to be applied prior to arrival of the additional perishable products, and wherein the at least one processor is configured to determine the temporary price based on the information about the displayed inventory and the information about the additional perishable products.

14. The system of claim 1 , wherein the at least one placement suggestion includes an indication of a location for placing the additional perishable products on the at least one shelving unit, and wherein the at least one processor is configured to determine the location for placing the additional perishable products based on the information about the displayed inventory and the information about the additional perishable products.

15. The system of claim 1 , wherein the at least one placement suggestion includes an indication of a size for a display area for the additional perishable products, and wherein the at least one processor is configured to determine the size for the display area based on the information about the displayed inventory and the information about the additional perishable products.

16. The system of claim 1 , wherein the at least one placement suggestion includes a price for the additional perishable products, and wherein the at least one processor is configured to determine the price for the additional perishable products based on the information about the displayed inventory and the information about the additional perishable products.

17. The system of claim 1 , wherein the at least one processor is further configured to:

obtain information about perishable products available in a storage area of the retail store; and

use the information about the perishable products available in the storage area and the information about the additional perishable products to determine at least one task for reorganizing perishable products in the retail store.

18. The system of claim 17 , wherein the at least one task for reorganizing perishable products includes a task for reorganizing perishable products currently available in the storage area.

19. The system of claim 17 , wherein the at least one task for reorganizing perishable products includes a task for reorganizing perishable products placed on the at least one shelving unit.

20. The system of claim 17 , wherein the at least one processor is further configured to:

predict a condition of the perishable products available in the storage area of the retail store at a time when the additional perishable products are scheduled to arrive; and

use the information about the perishable products available in the storage area, the information about the additional perishable products, and the predicted condition to determine at least one task for reorganizing perishable products in the retail store.

21. The system of claim 1 , wherein analyzing the set of images further includes using an optical character recognition (OCR) algorithm to extract textual information from at least one of the images.

22. The system of claim 1 , wherein the information about the displayed inventory includes demand indicators for multiple types of perishable products, and wherein the demand indicators are determined by running a simulation based on at least one forecast algorithm and demand history data associated with the perishable products.

23. The system of claim 1 , wherein analyzing the set of images further includes determining a ripeness associated with the plurality of perishable products based on determining that values of the set of images match values of the product quality profile pattern within a value threshold.

24. The system of claim 1 , wherein analyzing the set of images comprises constructing a three-dimensional image.

25. A method for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified perishable products, the method comprising:

receiving a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store;

analyzing the set of images to determine information about a displayed inventory of the plurality of perishable products, wherein analyzing the set of images includes comparing digital image data associated with the set of images to a product quality profile pattern, the product quality profile pattern having been generated by a machine learning model, the machine learning model having been trained using combinations of digital hyperspectral images and corresponding levels of product qualities including measured levels of hyperspectral wavelength information or waveband information;

obtaining information about additional perishable products scheduled to be displayed on the at least one shelving unit;

using the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and

providing the at least one suggestion to an entity associated with the retail store.

26. A computer program product for identifying perishable products in a retail store based on analysis of image data and for automatically generating placement suggestions relating to the identified perishable products embodied in a non-transitory computer-readable medium and executable by at least one processor, the computer program product including instructions for causing the at least one processor to execute a method comprising:

receiving a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store;

analyzing the set of images to determine information about a displayed inventory of the plurality of perishable products, wherein analyzing the set of images includes comparing digital image data associated with the set of images to a product quality profile pattern, the product quality profile pattern having been generated by a machine learning model, the machine learning model having been trained using combinations of digital hyperspectral images and corresponding levels of product qualities including measured levels of hyperspectral wavelength information or waveband information;

obtaining information about additional perishable products scheduled to be displayed on the at least one shelving unit;

using the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and

providing the at least one suggestion to an entity associated with the retail store.