Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. Graphical Abstract

In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

The Internet of Things (IoT) landscape is hindered by a critical challenge: the lack of semantic interoperability among diverse data models. Existing IoT solutions often function as isolated data silos, impeding the seamless integration of heterogeneous data sources crucial for informed decision-making and streamlined processes. This research addresses this issue by introducing a pioneering solution: the Semantic Interoperability Support System (SISS). SISS is an innovative tool designed to bridge the semantic divide between disparate data models within a common application domain. To address the lack of interoperability between current IoT platforms, devices, and solutions that use native data models, SISS facilitates integration by enabling the generation of gateways or translator components. These components establish mappings between the semantic properties of source and target data models, leveraging advanced semantic analysis and inference techniques. The core principle underpinning SISS is its ability to discern and map the semantic content of data models. Through a meticulous analysis of the temporal and spatial dimensions inherent in the data, SISS establishes meaningful connections. This innovative approach fosters interoperability and enables a deeper understanding of the underlying information, enhancing the potential for data-driven insights. This paper delves into the pervasive issue of semantic interoperability in the current IoT paradigm and presents SISS as a transformative solution. By emphasizing its ability to transcend the limitations of existing solutions and its methodology to generate mappings between disparate data models, this research contributes to the achievement of global semantic interoperability in IoT.

Modern agriculture and farmers have been benefiting from the broad possibilities that wireless sensor networks and Internet of Things, in general, can bring to this primary sector and associated businesses. This new approach, also known as precision farming, exploits multiple high-end technologies, typically centered on environmental data sensing of diverse nature, e.g., temperature, humidity, moisture and COx, which can provide valuable indicators regarding the conditions where crops and livestock are produced. For that purpose, the respective sensors can be deployed in specific places in a farm, remaining static, or they can be placed on board of vehicles, such as tractors and unmanned aerial vehicles. Such sensors can also be integrated into wearable devices that are put on livestock individuals to track their positions, behavior and physiological aspects. Furthermore, this advanced technology-based approach to farming includes the respective data processing and analytics, in many cases employing machine learning and artificial intelligence techniques and algorithms, namely to provide the farmer with appropriate and reliable decision support systems. The work presented here focus specifically on a mobile application, which allows to collect inputs from users at the field and includes such human assessments in the automated loop of the sensing data flow in a precision farming system. The developed solution enables new functionalities on such systems, namely towards data auditing and labelling, that can be initiated directly from the field, which is where the sensors are and the respective sensing conditions can be assessed by the mobile app user.

Structured data embodies vast reservoirs of actionable knowledge, yet non-technical users often lack the tools to effectively access or leverage these resources. This paper presents a novel middleware system architecture that harnesses Large Language Models (LLMs) to translate natural language queries (NLQs) into executable SQL statements, enabling seamless interaction with relational databases without requiring technical expertise. To bridge a critical gap in existing research, the proposed system integrates a robust security framework featuring multi-factor authentication and fine-grained, role-based access control. A comprehensive evaluation was conducted using over 813,000 real spatio-temporal IoT samples from the H2020-AFarCloud international project, comparing both general-purpose LLMs and models fine-tuned for NLQ-to-SQL translation. Two complementary evaluation metrics—Execution Accuracy (EX) and Valid Efficiency Score (VES)—were employed to assess syntactic correctness, semantic fidelity, and runtime efficiency. Experimental results show that GPT-o3 mini-high achieves the highest overall performance in terms of average VES, while DeepSeek R1 exhibits the most consistent behavior across heterogeneous query scenarios, as reflected by lower performance variability. These findings underscore the interplay between model capability and computational efficiency, demonstrating the potential of LLM-driven middleware to democratize structured data access and enhance data-driven decision-making.