@article {844, title = {AI and Related Technologies in the Fields of Smart Agriculture: A Review}, journal = {Information}, volume = {16}, year = {2025}, pages = {100}, abstract = {

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.

}, 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 {861, title = {Automated Test Generation and Marking Using LLMs}, journal = {Electronics}, volume = {14}, year = {2025}, pages = {2835}, abstract = {

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements{\textendash}compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system{\textquoteright}s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17\% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5\% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s.

}, keywords = {artificial intelligence, DeepSeek, exams, large language models, Llama, named entity recognition, Natural language processing, question{\textendash}answer generation, retrieval-augmented generation, Semantic Similarity, test generation, test marking}, issn = {2079-9292}, doi = {https://doi.org/10.3390/electronics14142835}, author = {Papachristou, Ioannis and Dimitroulakos, Grigoris and Vassilakis, Costas} } @article {842, title = {Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects}, journal = {Electronics}, volume = {13}, year = {2024}, pages = {4362}, abstract = {

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70\% by 2050, AI technologies{\textemdash}including machine learning, big data analytics, and the Internet of things (IoT){\textemdash}offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI{\textquoteright}s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming.

}, keywords = {artificial intelligence, precision agriculture, sustainability, value chain}, issn = {2079-9292}, doi = {10.3390/electronics13224362}, author = {Assimakopoulos, Fotis and Costas Vassilakis and Dionisis Margaris and Kotis, Konstantinos and Spiliotopoulos, Dimitris} } @article {818, title = {Disruptive Technologies for Parliaments: A Literature Review}, journal = {Future Internet}, volume = {15}, year = {2023}, month = {feb}, pages = {66}, abstract = {

Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures and processes. Additionally, the use of inclusive participatory organisational models is essential for the effective adoption of these technologies. Such models aim to transform organisational structures, as well. Public organisations, such as the parliament, could utilise information systems{\textquoteright} personalisation techniques. As there are a lot of efforts to define the framework, the methodology, the techniques, the platforms, and the suitable models for digital technologies adoption in public organisations, this paper aims to provide a literature review for disruptive technology inclusive use in parliaments. The review emphasises the assessment of the applicability of the technologies, their maturity and usefulness, user acceptance, their performance, and their correlation to the adoption of relevant innovative, inclusive organisational models. It is argued that the efficient digital transformation of democratic institutions, such as parliaments, with the use of advanced e-governance tools and disruptive technologies, requires strategic approaches for adoption, acceptance, and inclusive service adaptation.

}, keywords = {artificial intelligence, digital transformation, inclusiveness, Internet of Things, parliament, Recommender Systems}, doi = {https://doi.org/10.3390/fi15020066}, url = {https://www.mdpi.com/1999-5903/15/2/66}, author = {Dimitris Koryzis and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos} }