In the fast-paced world of fresh produce logistics, maintaining optimal conditions during transportation is crucial to prevent spoilage and ensure quality delivery. A recent study published in the journal ‘Sensors’ introduces an innovative Internet of Things (IoT) platform designed to revolutionize the monitoring and energy management of fresh fruit and vegetable transportation. Led by Chayapol Kamyod from the School of Applied Digital Technology at Mae Fah Luang University in Thailand, this research presents a self-contained, battery-powered IoT system that offers real-time monitoring and AI-driven energy consumption prediction.
The IoT device, which operates independently of vehicle power, continuously logs temperature, relative humidity, GPS position, and onboard power draw. This data is then used to train and compare various machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR), to predict energy consumption under varying environmental and routing conditions. The results were impressive, with GBM and LR achieving high explanatory power and a mean absolute error of 0.77 A·h. The Random Forest model provided interpretable feature importance data, identifying temperature as the dominant driver of energy consumption, followed by trip duration and humidity.
“This system not only monitors the conditions inside the truck but also predicts energy consumption, allowing for proactive decision-making,” said Kamyod. “By understanding the key factors that influence energy use, we can optimize routes and conditions to extend the shelf life of perishable goods and reduce energy costs.”
The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. This comprehensive platform supports proactive decision-making in perishable logistics, enabling stakeholders to make informed choices that can significantly impact the bottom line.
The commercial implications of this research are substantial. For the agriculture sector, which often grapples with the challenges of perishable logistics, this IoT-based system offers a powerful tool to enhance efficiency and reduce waste. By continuously monitoring and predicting conditions, farmers and logistics providers can ensure that fresh produce arrives at its destination in optimal condition, thereby minimizing losses and maximizing profits.
Moreover, the AI-driven energy consumption prediction can lead to significant cost savings. By understanding the factors that drive energy use, companies can optimize their operations to reduce energy consumption and lower operational costs. This is particularly important in an era where sustainability and cost-efficiency are top priorities for businesses.
The research also opens up new avenues for future developments in the field. As Kamyod noted, “The ability to configure sampling/transmit cadence to preserve autonomy under stressful conditions is a game-changer. This adaptability ensures that the system can operate effectively even in challenging environments, providing reliable data and insights.”
In conclusion, this innovative IoT platform represents a significant advancement in the field of perishable logistics. By leveraging real-time monitoring and AI-driven predictions, it offers a powerful tool for optimizing the transportation of fresh produce, reducing waste, and lowering costs. As the agriculture sector continues to evolve, such technologies will play a crucial role in enhancing efficiency and sustainability, ultimately benefiting both businesses and consumers.