System and method for identifying fruit shelf life

A system for determining shelf life of a fruit, said system comprising:

• a first receiver (202) for receiving an input from a user to take images of one or more fruits whose shelf life is to be ascertained;
• a) a thermal imaging device (204) configured to capture thermal images (206) of one or more fruits on receiving the input by the receiver (202), wherein said thermal imaging device (204) is provided with a motor configured to rotate 360 degrees to assist said thermal imaging device (204) to capture thermal images (206) of said one or more fruits from all directions, wherein said thermal imaging device (204) converts infrared radiations released by the fruit and backgrounds and surrounds of said fruit into a visible thermal image (206);
• b) a first transmitter (208) operationally interconnected to a thermal imaging device (204) to transmit captured images by said thermal imaging device (204) to a server called as processing unit (210);
• c) a processing unit (210) configured to ascertain shelf life of said one or more fruits based on the received thermal images (206), wherein said processing unit (210) ascertains shelf life of one or more fruits;
• by:
• determining the maximum and minimum temperature value of the fruit along with the temperature distribution of entire fruit to differentiate whether scanned fruit is from cold storage or normal temperature automatically;
• classifying acquired thermal images (206) of fruits into a plurality of classes means number of days the fruit will remain edible as particular fruit's shelf-life;
• wherein this is done by:
• comparing detailed temperature distribution with pre-defined threshold values also called as weights obtained by trained model by applying deep learning techniques on thermal dataset of the same standard fruit on the server (210) to determine the shelf life of the fruit;
• wherein thermal dataset comprises samples of several thermal images of fruits taken on every day after harvesting where the fruit may be from cold storage or room temperature, these thermal mages are then used to train the deep learning model;
• a display unit (214) to display determined shelf life of the fruit;