Publications
A Novel Approach for Leveraging Agent-Based Experts on Large Language Models to Enable Data Sharing Among Heterogeneous IoT Devices in Agriculture
This paper by Nur Arifin Akbar, Biagio Lenzitti, and Domenico Tegolo explores a new way to improve data sharing among different Internet of Things devices in agriculture using agent-based experts supported by large language models (LLMs). By combining autonomous “agents,” decentralized data storage, and LLMs for understanding queries, the framework aims to boost collaboration, data integration, and more informed decision-making. Tests on synthetic farming data showed clear gains in diagnosing plant diseases and providing tailored precision-farming advice. However, challenges relating to data privacy, standardization, and incentives still need to be addressed for wider real-world use.
Data value creation in agriculture: A review
Havva Uyar, Ioannis Karvelas, Stamatia Rizou, Spyros Fountas
Agricultural data is a valuable resource for driving innovation, enhancing efficiency and promoting sustainability in farming. But how is the value of this data perceived and implemented in agriculture? This review article, authored by EnTrust DC Havva Uyar, investigates key ways agricultural data generates value, such as improving processes, predicting outcomes, and tailoring solutions. The research highlights that agricultural data grows more valuable with reuse, urging a shift in focus from data ownership to the ownership of the value derived from the data. These insights highlight the need for robust frameworks and further research to realise the vast potential and value of agri-data.
Building trust: A systematic review of the drivers and barriers of agricultural data sharing
This systematic review by EnTrust DC Clare Sullivan, examines the key drivers and barriers to sharing agricultural data in smart farming. The research identifies factors like improved decision-making, collaboration, and clear data ownership as motivators, while mistrust, privacy concerns, and technical challenges act as obstacles. The findings highlight the need for better communication, trust-building, and practical solutions to encourage agricultural data sharing. These insights aim to support policies and practices that make agri-data sharing more accessible and effective.
Modified xLSTM for Compression and Decompression of Multimodal Agricultural Data in Low-Resource Settings
This paper by Nur Arifin Akbar, Biagio Lenzitti, Domenico Tegolo, Rahool Dembani, and Clare S. Sullivan addresses the challenge of data management in agricultural settings with limited technological infrastructure. By developing a novel compression technique using a modified extended Long Short-Term Memory (xLSTM) network with multiplicative LSTM cells, the researchers created an innovative approach to compress text and image data from agricultural datasets. The method results in significant reductions in the size of data while maintaining acceptable reconstruction quality. This is particularly useful for precision agriculture and remote monitoring in resource-constrained environments. The approach shows promise in improving data handling efficiency, though challenges remain in preserving fine image details and expanding the text vocabulary.