Remaining Shelf Life Estimation of Fresh Fruits and Vegetables during Transportation
DOI:
https://doi.org/10.69996/ijari.2026006Keywords:
IoT, Accuracy, Machine Learning, Models, Statistical Analysis, Health, InformaticsAbstract
This study proposes a novel approach for estimating the remaining shelf life of fresh fruits and vegetables during transportation, addressing a critical need in the food supply chain. Leveraging advanced data analytics and machine learning techniques, the proposed system incorporates various environmental factors such as temperature, humidity, and transportation conditions to predict the degradation of freshness and nutritional quality of perishable goods over time. By analyzing real-time sensor data from IoT devices installed in transportation vehicles, the system accurately assesses the deterioration rate of fresh produce and calculates its remaining shelf life. Additionally, the system utilizes historical data and predictive modeling to account for variations in transportation conditions and product characteristics, enhancing the accuracy and reliability of shelf life estimations. Through comprehensive experimentation and validation, this research aims to demonstrate the effectiveness and practicality of the proposed system in improving supply chain management, reducing food waste, and ensuring the delivery of high-quality fresh produce to consumers.
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