The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in smart sensing technologies for arable crops and grasslands. We analyzed the peer-reviewed literature published between 2020 and 2024, focusing on the adoption of IoT-based sensor networks and AI-driven analytics across various agricultural applications. The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. However, this review also identifies critical challenges, including high infrastructure costs, limited interoperability, connectivity constraints in rural areas, and ethical concerns regarding transparency and data privacy. To address these barriers, recent innovations have emphasized the potential of Edge AI for local inference, blockchain systems for decentralized data governance, and autonomous platforms for field-level automation. Moreover, policy interventions are needed to ensure fair data ownership, cybersecurity, and equitable access to smart farming tools, especially in developing regions. This review is the first to systematically examine AI-integrated sensing technologies with an exclusive focus on arable crops and grasslands, offering an in-depth synthesis of both technological progress and real-world implementation gaps.

The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Drought is an increasingly critical global challenge, significantly impacting agricultural productivity, food security, and ecosystem stability. As climate change intensifies the frequency and severity of drought events, innovative strategies are essential to enhance plant resilience and sustain agricultural systems. This review explores the vital role of beneficial microbes in conferring drought tolerance, focusing on Plant Growth-Promoting Rhizobacteria (PGPR), mycorrhizal fungi, endophytes, actinomycetes, and cyanobacteria. These microorganisms mitigate drought stress through diverse mechanisms, including osmotic adjustment, enhancement of root architecture, modulation of phytohormones, induction of antioxidant defenses, and regulation of stress-responsive gene expression. Ecological and agricultural innovations leveraging these beneficial microbes have demonstrated significant potential in bolstering drought resilience. Strategies such as soil microbiome engineering, bioaugmentation, and the integration of microbial synergies within pest management frameworks enhance ecosystem resilience and agricultural sustainability. Additionally, advancements in agricultural practices, including seed coating, soil amendments, the development of microbial consortia, and precision agriculture technologies, have validated the effectiveness and scalability of microbial interventions in diverse farming systems. Despite promising advancements, several challenges hinder the widespread adoption of microbial solutions. Environmental variability can affect microbial performance, necessitating the development of robust and adaptable strains. Scale-up and commercialization hurdles, economic constraints, and regulatory and safety considerations also pose significant barriers. Furthermore, the complex interactions between microbes, plants, and their environments require a deeper understanding to optimize microbial benefits consistently. Future research should focus on integrating cutting-edge technologies such as genomics, synthetic biology, and precision agriculture to refine and enhance microbial interventions. Collaborative efforts among academia, industry, and government are essential to bridge the gap between research and practical implementation. By addressing these challenges and harnessing microbial innovations, it is possible to develop resilient and sustainable agricultural systems capable of thriving in an increasingly water-scarce world.

The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations.

The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles.