Machine Learning-Driven Daily Demand Forecasting for Fresh Produce: A Case Study with Bananas

Accurate demand forecasting is crucial for reducing food waste, enhancing resilience, and promoting sustainability in food systems. Relying only on historical sales data is inadequate to predict the demand for perishable food products. The remaining shelf-life of food products and their qualities need to be considered as they play a vital role in the forecasting process, leading to accurate and reliable predictions. This paper proposes a four-stage conceptual framework to predict the daily demand for fresh food items, incorporating two critical variables: health class and remaining shelf life. The framework is applied to a real industry case to monitor the freshness quality of bananas, and important variables are collected during run-time. The collected data are then processed and used to train several classification models to classify the health class of bananas into either fresh, ripening, or spoiled. Not very surprisingly as many other application domains, results reveal that the random forest model outperforms other models in predicting the health class of bananas placed in boxes, achieving about 91 % prediction accuracy. However, this paper presents a first research of its kind with the abundance of real data, and specific comprehensiveness and focus on the overdue industrial problem.