A Machine Learning Framework for the Optimization of Postharvest Cold Chain Systems: An Artificial Neural Network Approach to Perishable Commodity Preservation

Abstract

Postharvest losses of perishable commodities remain a major global challenge due to inefficiencies in conventional cold chain systems, necessitating intelligent, adaptive technologies for improved preservation. This study aims to develop a machine learning framework using an Artificial Neural Network (ANN) to optimize refrigeration performance for vegetable preservation, with specific objectives to model nonlinear interactions among operational parameters, improve prediction accuracy for quality indicators, and identify optimal operating conditions that balance energy use and product longevity. Using a 30-run Design of Experiments (DOE) dataset, the ANN was trained in Python (TensorFlow/Keras) with inputs including evaporator temperature, cooling duration, insulation thickness, airflow rate, and storage load, and outputs comprising coefficient of performance (COP), moisture loss, energy consumption, and shelf life. Model evaluation using MSE, RMSE, MAE, and R² revealed inconsistent performance, with some outputs initially achieving high predictive accuracy while later metrics showed negative R² for moisture loss and shelf life, indicating overfitting and data limitations; however, feature importance analysis and 3-D response surfaces confirmed meaningful nonlinear relationships, and optimization results identified settings such as −3.69°C evaporator temperature and moderate storage load for maximizing COP, and −2.14°C for minimizing moisture loss. The findings demonstrate the potential of ANN in multi-objective cold-chain optimization but highlight the need for larger datasets, improved network regularization, and integration with IoT-based real-time monitoring. It is therefore recommended that future work employ expanded experimental data, robust ANN architectures, and sensor-driven dynamic updating to enhance generalization and practical deployment in commercial cold-chain environments.