DEEP CNN MODEL FOR PREDICTING SHELF LIFE OF FRESH FRUITS AND VEGETABLES USING TEMPERATURE SIMULATION DATA FOR OPTIMIZED TRANSPORT AND STORAGE

Abstract

India is one of the largest producers of fruits and vegetables globally, contributing about 14% of global production., a significant portion of this produce, approximately 30-40%, is lost due to inefficient storage and transportation systems, resulting in an economic loss of ₹92,651 crores (2018).  The objective is to develop a deep learning-based model that uses temperature simulation data to accurately predict the shelf life of fresh fruits and vegetables, ensuring optimized transport and storage conditions.  The title refers to a machine learning-based approach, specifically using deep CNN models, to predict how long fresh produce (fruits and vegetables) will remain viable based on temperature data collected during storage and transport. This system helps in making decisions to reduce spoilage and improve logistics.