Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.

The emergence of Industry 4.0 (I4.0) has significantly transformed manufacturing landscapes, introducing interconnected, dynamic, and data-rich environments. This paper focuses on the application of Industrial Machine Learning (I-ML) within these evolving manufacturing contexts, exploring both the challenges and future prospects of its integration. A systematic literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, forms the foundation of our analysis, characterizing the role of Machine Learning (ML) in modern manufacturing, its current challenges, and future trends. This research delves into the implications of I-ML in various manufacturing scenarios, including predictive maintenance, anomaly detection, and quality control, providing a comprehensive overview of practical applications along with an identification of related emerging technologies and trends. We also address the critical need for sustainable, reproducible, and reliable performance in industrial applications and explore strategies for overcoming barriers to ML adoption in the industry. Recommendations for future research directions are provided, aiming to bridge the gap between ML advancements and their practical, scalable implementation in industrial settings, paving the way to future research in the field. Lastly, we aim to contribute to the identification of challenges and future research directions for the ongoing digital transformation of manufacturing industries, offering insights into how ML can be effectively leveraged in the era of I4.0.

The agricultural sector worldwide faces serious problems regarding water scarcity, which demands innovative management methods to optimise water use. In response, we propose the Intelligent Data-Driven Decision Support for Agricultural Systems (ID3SAS) methodology, which offers a scalable, flexible, and cloud-based decision support system for real-time supervision and control in agricultural environments. Aligned with the prevailing trends of Agriculture 4.0, ID3SAS integrates data acquisition, cloud-based storage, machine learning, predictive analysis, and run-time reasoning to facilitate decision-making processes, thereby assisting users in making more informed and sustainable decisions. In a case study with tomato plants, ID3SAS-irrigated plants showed 20.9% reduction in water consumption and 26.4% increase in crop production compared to traditional methods, which despite the controlled laboratory environment setting, highlights the methodology’s promising potential in addressing water scarcity and enhancing agricultural productivity.

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

The agricultural sector faces significant challenges, including resource inefficiency, unpredictable weather conditions, and the need for sustainable practices. These issues necessitate the application of advanced methods introduced by Agriculture 4.0 to ensure productivity and sustainability. This paper focus on the application of the Intelligent Data-Driven Decision Support System for Agricultural Systems (ID3SAS) methodology to a proximal sensing case study aimed at improving vineyard management via monitoring and predictive modeling with Artificial Intelligence. The developed system was deployed in vineyards in Portugal, and provided a robust test-bed for real-world application.