Application of the IoT in the Food Supply Chain-From the Perspective of Carbon Mitigation

With the rising demands on supply chain transparency and food security, the rapid outspread of the Internet of Things (IoT) to improve logistical efficiency, and the rising penetration of sensor technology into daily life, the extensive integration of the IoT in the food sector is well anticipated. A perspective on potential life cycle trade-offs in regard to the type of integration is necessary. We conduct life cycle assessment (LCA) integrated with shelf life-food loss (SL-FL) models, showing an overall 5-fold leverage on carbon reduction, which is diet dependent and a function of income. Meat presents the highest leverage, 35 +/- 11-times, owing to its high carbon footprint. Two-thirds (65%) of global sensors (1 billion) engaged in monitoring fruits and vegetables can mitigate less than 7% of the total reduced carbon emissions. Despite the expected carbon emission reductions, widespread adoption of the IoT faces multiple challenges such as high costs, difficulties in social acceptance, and regional variability in technological development. Furthermore, changes in the distribution of transportation resources and dealer service models, requirements regarding the accuracy of sensor data analysis, efficient and persistent operation of devices, development of agricultural infrastructure, and farmer education and training have all increased uncertainty. Nonetheless, the research trend in smart sensors toward smaller chips and the potential integration of machine learning or blockchain as further steps make it possible to leverage these advantages to facilitate market penetration. These insights facilitate the future optimization of the application of IoT sensors for sustainability.

Automated summary

With the increasing demands for supply chain transparency and food security, the extensive integration of the Internet of Things (IoT) in the food sector is expected. A study combining life cycle assessment (LCA) and shelf life-food loss (SL-FL) models shows a potential 5-fold leverage on carbon reduction, dependent on diet and income. However, widespread adoption of the IoT faces challenges such as high costs, social acceptance difficulties, and regional variability in technological development. The research trend towards smaller chips and the integration of machine learning or blockchain technologies could facilitate market penetration.