Néstor Lucas Martínez

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.

Undoubtedly, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably, adapting to climate change, and avoiding food waste. As advanced information systems and Internet technologies are adopted in Agriculture 4.0, enormous farming data, such as meteorological information, soil conditions, marketing demands, and land uses, can be collected, analyzed, and processed for assisting farmers in making appropriate decisions and obtaining higher profits. Therefore, agricultural decision support systems for Agriculture 4.0 has become a very attractive topic for the research community. The objective of this paper aims at exploring the upcoming challenges of employing agricultural decision support systems in Agriculture 4.0. Future researchers may improve the decision support systems by overcoming these detected challenges. In this paper, the systematic literature review technique is used to survey thirteen representative decision support systems, including their applications for agricultural mission planning, water resources management, climate change adaptation, and food waste control. Each decision support system is analyzed under a systematic manner. A comprehensive evaluation is conducted from the aspects of interoperability, scalability, accessibility, usability, etc. Based on the evaluation result, upcoming challenges are detected and summarized, suggesting the development trends and demonstrating potential improvements for future research.

In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV’s planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission (ρs=0.79, n=45, p<0.001). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.