@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 {847, title = {Fine-Tuning Large Language Models for Ontology Engineering: A Comparative Analysis of GPT-4 and Mistral}, journal = {Applied Sciences}, volume = {15}, year = {2025}, month = {February}, pages = {2146}, abstract = {

Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question{\textendash}answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models{\textquoteright} abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency.

}, keywords = {domain-specific knowledge, large language models fine-tuning, ontology engineering, search and rescue}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app15042146}, author = {Doumanas, Dimitrios and Soularidis, Andreas and Spiliotopoulos, Dimitris and Costas Vassilakis and Kotis, Konstantinos} } @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 {836, title = {The Implementation of {\textquotedblleft}Smart{\textquotedblright} Technologies in the Agricultural Sector: A Review}, journal = {Information}, volume = {15}, year = {2024}, pages = {466}, abstract = {

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.

}, 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} }