Abstract: This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.

Abstract: Milk is consumed in large quantities across the world and quality of milk is important to consumer. Multilayer packaging is one of solution to maintain quality of milk for prolonged period. However, quality of milk may change due to issues in handling, transportation, environment, and packaging. Currently available techniques fail to detect milk quality when packaging is opaque. Hence, common methods that are used for detecting quality of milk cannot be easily used over packaged milk. Present application provides non-invasive method and system for detecting quality of packaged milk. More specifically, system uses a sensing technique enabled in a capacitive Near-field communication (NFC) tag and an NFC enabled device for detecting quality of the packaged milk in real-time. In particular, the capacitive NFC tag is configured to provide milk quality information on NFC enabled device when NFC enabled device comes near capacitive NFC tag configured on packaged container.

Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.