Predicting Shelf-Life of Fruits: A Multifactorial Approach using Intrinsic and Extrinsic Determinants

Abstract

Abstract: Predicting the shelf life of fruits is crucial for maintaining food freshness and reducing waste in the supply chain. This paper presents a novel approach to predicting fruit shelf life using a combination of fruit characteristics and environmental factors. Firstly, the freshness of fruits is assessed based on their physical attributes such as size, shape, and color. Next, a predictive model is developed to estimate the number of days a fruit remains edible in a given cold storage system, considering temperature variations. The proposed method leverages machine learning techniques to analyze historical data and identify patterns that influence fruit degradation over time. Experimental results demonstrate the effectiveness of the proposed approach in accurately predicting fruit shelf life, thus facilitating better inventory management and reducing food wastage. This research contributes to the optimization of fruit storage practices, ultimately leading to improved food quality and sustainability in the agricultural sector