@article {844,
	title = {AI and Related Technologies in the Fields of Smart Agriculture: A Review},
	journal = {Information},
	volume = {16},
	year = {2025},
	pages = {100},
	abstract = {<p>The integration of cutting-edge technologies{\textemdash}such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies{\textemdash}is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies. The areas examined in this study are open field smart farming, vertical and indoor farming, zero waste agriculture, precision livestock farming, smart greenhouses, and regenerative agriculture. This paper links current research, technological innovations, and case studies to present a comprehensive review of these emerging technologies being developed in the context of smart agriculture, for the benefit of farmers and consumers in general. By exploring practical applications and future perspectives, this work aims to provide valuable insights to address global food security challenges, minimize environmental impacts, and support sustainable development goals through the application of new technologies.</p>
},
	keywords = {AI, artificial intelligence, indoor farming, IoT, precision agriculture, precision livestock, regenerative agriculture, smart farming, smart green, vertical farming, zero-waste agriculture},
	issn = {2078-2489},
	doi = {https://doi.org/10.3390/info16020100},
	author = {Assimakopoulos, Fotis and Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris}
}
@article {836,
	title = {The Implementation of {\textquotedblleft}Smart{\textquotedblright} Technologies in the Agricultural Sector: A Review},
	journal = {Information},
	volume = {15},
	year = {2024},
	pages = {466},
	abstract = {<p>The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring.</p>
},
	keywords = {Agriculture 4.0, IoT, precision agriculture, smart farming},
	issn = {2078-2489},
	doi = {https://doi.org/10.3390/info15080466},
	url = {https://www.mdpi.com/2078-2489/15/8/466},
	author = {Assimakopoulos, Fotis and Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris}
}
@article {Vassilakis_fgcs17,
	title = {exhiSTORY: Smart Exhibits That Tell Their Own Stories},
	journal = {Future Generation of Computer Systems},
	volume = {81},
	year = {2018},
	pages = {542-556},
	abstract = {Museum exhibitions are designed to tell a story; this story is woven by curators and in its context a particular aspect of each exhibit, fitting to the message that the story is intended to convey, is highlighted. Adding new exhibits to the story requires curators to identify for each exhibit its aspects that fit to the message of the story and position the exhibit at the right place in the story thread. The availability of rich semantic information for exhibits, allows for exploiting the wealth of meanings that museum exhibits express, enabling the automated or semi-automated generation of practically countless stories that can be told. Personalization algorithms can then be employed to choose from these stories the ones most suitable for each individual user, based on the semantics of the stories and information within the user profile. In this work we examine how opportunities arising from technological advances in the fields of IoT and semantics can be used to develop smart, self-organizing exhibits that cooperate with each other and provide visitors with comprehensible, rich, diverse, personalized and highly stimulating experiences. These notions are included in the design of a system named exhiSTORY, which also exploits previously ignored information and identifies previously unseen semantic links. We present the architecture of the system and discuss its application potential.},
	keywords = {Cultural heritage, IoT, personalization, Quality of Experience (QoE), Self-organizing exhibitions, semantics},
	doi = {https://doi.org/10.1016/j.future.2017.10.038},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X17324093},
	author = {Costas Vassilakis and Vassilis Poulopoulos and Angela Antoniou and Manolis Wallace and Giorgos Lepouras and Martin Lopez Nores}
}
@conference {591,
	title = {A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence},
	booktitle = {In Proceedings of the IEEE Workshop on Cyber Security \& Resilience in the Internet of Things (CSRIoT @ IEEE Services)},
	year = {2016},
	month = {July 2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Milan, Italy},
	abstract = {The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that {\textendash}given the appropriate tools and methods{\textendash} may be identified, crawled and subsequently leveraged to actionable cyber- threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker fo- rums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state- of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.},
	keywords = {crawling architecture, cyber-security, cyber-threat intelligence, IoT, language models, Machine Learning},
	url = {http://users.uop.gr/ trifon/papers/pdf/csriot19-KCTS.pdf},
	author = {Paris Koloveas and Thanasis Chantzios and Christos Tryfonopoulos and Spiros Skiadopoulos}
}
