Dynamic product lifecycle predictions based on real-time product quality analysis

1. A method for lifecycle prediction, the method comprising:

identifying a unit of a product that has been selected from a store bin;

retrieving data from at least one connected Internet of Things (IoT) device and a connected infrared scanner;

predicting a lifecycle of the identified unit using a multi-linear regression technique, wherein the lifecycle is a personalized lifecycle of the identified unit based on more than one factor, wherein each factor is assigned a weight, and wherein the weight of each factor is dynamically adjusted;

displaying the lifecycle on the at least one connected IoT device or the infrared scanner;

determining whether the unit is added to a cart; and

pushing the retrieved data to a point of sale for generating data at checkout.

2. The method of claim 1 , wherein retrieving data from at least one connected Internet of Things (IoT) device and the connected infrared scanner further comprises:

collecting one or more images, from an IoT camera, depicting color, size, and quantity information of the identified unit;

collecting one or more thermal images, from the connected infrared scanner, depicting ripening, flavor, and texture of the identified unit; and

analyzing the retrieved data.

3. The method of claim 1 , wherein the multi-linear regression technique is a supervised machine learning model, implemented with a plurality of known input and output data, to predict the personalized lifecycle of the identified unit.

4. The method of claim 1 , wherein displaying the lifecycle on the at least one connected IoT device or the infrared scanner further comprises:

digitally displaying the lifecycle on a storage bin for the identified unit or the infrared scanner; and

sending, based on a predefined user preference, an electronic message to a user.

5. The method of claim 1 , wherein determining whether the unit is added to the cart further comprises:

retrieving, from the at least one connected Internet of Things (IoT) device and the connected infrared scanner, images of the identified unit in the cart.

6. The method of claim 1 , wherein pushing the retrieved data to the point of sale for generating data at checkout further comprises:

generating an invoice which includes the lifecycle of the identified unit, wherein the invoice is a receipt, delivered electronically to a user upon checkout.

7. A computer system for lifecycle prediction, comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:

identifying a unit of a product that has been selected from a store bin;

retrieving data from at least one connected Internet of Things (IoT) device and a connected infrared scanner;

predicting a lifecycle of the identified unit using a multi-linear regression technique, wherein the lifecycle is a personalized lifecycle of the identified unit based on more than one factor, wherein each factor is assigned a weight, and wherein the weight of each factor is dynamically adjusted;

displaying the lifecycle on the at least one connected IoT device or the infrared scanner;

determining whether the unit is added to a cart; and

pushing the retrieved data to a point of sale for generating data at checkout.

8. The computer system of claim 7 , wherein retrieving data from at least one connected Internet of Things (IoT) device and the connected infrared scanner further comprises:

collecting one or more images, from an IoT camera, depicting color, size, and quantity information of the identified unit;

collecting one or more thermal images, from the connected infrared scanner, depicting ripening, flavor, and texture of the identified unit; and

analyzing the retrieved data.

9. The computer system of claim 7 , wherein-the multi-linear regression technique is a supervised machine learning model, implemented with a plurality of known input and output data, to predict the personalized lifecycle of the identified unit.

10. The computer system of claim 7 , wherein displaying the lifecycle on the at least one connected IoT device or the infrared scanner further comprises:

digitally displaying the lifecycle on a storage bin for the identified unit or the infrared scanner; and

sending, based on a predefined user preference, an electronic message to a user.

11. The computer system of claim 7 , wherein determining whether the unit is added to the cart further comprises:

retrieving, from the at least one connected Internet of Things (IoT) device and the connected infrared scanner, images of the identified unit in the cart.

12. The computer system of claim 7 , wherein pushing the retrieved data to the point of sale for generating data at checkout further comprises:

generating an invoice which includes the lifecycle of the identified unit, wherein the invoice is a receipt, delivered electronically to a user upon checkout.

13. A computer program product for lifecycle prediction, comprising:

one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:

identifying a unit of a product that has been selected from a store bin;

retrieving data from at least one connected Internet of Things (IoT) device and a connected infrared scanner;

predicting a lifecycle of the identified unit using a multi-linear regression technique, wherein the lifecycle is a personalized lifecycle of the identified unit based on more than one factor, wherein each factor is assigned a weight, and wherein the weight of each factor is dynamically adjusted;

displaying the lifecycle on the at least one connected IoT device or the infrared scanner;

determining whether the unit is added to a cart; and

pushing the retrieved data to a point of sale for generating data at checkout.

14. The computer program product of claim 13 , wherein retrieving data from at least one connected Internet of Things (IoT) device and the connected infrared scanner further comprises:

collecting one or more images, from an IoT camera, depicting color, size, and quantity information of the identified unit;

collecting one or more thermal images, from the connected infrared scanner, depicting ripening, flavor, and texture of the identified unit; and

analyzing the retrieved data.

15. The computer program product of claim 13 , wherein the multi-linear regression technique is a supervised machine learning model, implemented with a plurality of known input and output data, to predict the personalized lifecycle of the identified unit.

16. The computer program product of claim 13 , wherein displaying the lifecycle on the at least one connected IoT device or the infrared scanner further comprises:

digitally displaying the lifecycle on a storage bin for the identified unit or the infrared scanner; and

sending, based on a predefined user preference, an electronic message to a user.

17. The computer program product of claim 13 , wherein determining whether the unit is added to the cart further comprises:

retrieving, from the at least one connected Internet of Things (IoT) device and the connected infrared scanner, images of the identified unit in the cart.