Spatio-temporal semantic data management systems for IoT in agriculture 5.0: Challenges and future directions

The Agri-Food sector is in a stressful situation due to the high demand for food from the growing population around the world. The agricultural sector is facing a challenging situation; it must increase production and reduce its impact on the environment by appropriately allocating resources, adapting to climate change, and avoiding food waste. Agriculture 5.0, as the fifth agricultural evolution, aims to offer a perfect symbiosis between agriculture, advanced technologies, and sustainability. The most advanced technologies in automation, monitoring, and decision support are driven by the collection and processing of large volumes of agricultural data, such as weather information, farm machinery, soil and crop conditions, and marketing demand for higher profits. Taking advantage of the technological paradigm of the Internet of Things, agricultural data provides information on spatial, temporal, and semantic dimensions. Spatio-temporal semantic data management systems have become the cornerstone for the achievement of Agriculture 5.0 through advanced Internet of Things technologies. This paper aims to review the current literature on spatio-temporal semantic data management systems for Agriculture 5.0. This paper uses a systematic literature review technique to study eleven representative spatio-temporal semantic data management systems. A comprehensive evaluation of the aspects of interoperability, accessibility, scalability, real-time operation capability, etc. is carried out. Based on the evaluation results, future challenges are detected and development trends and possible improvements are proposed for future research. Finally, a distributed architecture capable of satisfying the above needs and challenges is proposed. The paper aims to inspire further research and development efforts to improve the efficiency, accessibility, and performance of spatio-temporal semantic data management systems.