Agriculture 5.0 refers to the next phase of agricultural development, building upon the previous digital revolution in the agrarian sector and aiming to transform the agricultural industry to be smarter, more effective, and ecologically conscious. Farming processes have already started becoming more efficient due to the development of digital technologies, including big data, artificial intelligence (AI), robotics, the Internet of Things (IoT), and virtual and augmented reality. Farmers can make the most of the resources at their disposal thanks to this data-driven approach, allowing them to effectively cultivate and sustain crops on arable land. The European Union (EU) aims to make food systems fair, healthy, and environmentally sustainable through the Green Deal and its farm-to-fork, soil, and biodiversity strategies, zero pollution action plan, and upcoming sustainable use of pesticides regulation. Many of the historical synthetic pesticides are not currently registered in the EU market. In addition, the continuous use of a limited number of active ingredients with the same mode of action scales up pests/pathogens/weed resistance potential. Increasing plant protection challenges as well as having fewer chemical pesticides to apply require innovation and smart solutions for crop production. Biopesticides tend to pose fewer risks to human health and the environment, their efficacy depends on various factors that cannot be controlled through traditional application strategies. This paper aims to disclose the contribution of robotic systems in Agriculture 5.0 ecosystems, highlighting both the challenges and limitations of this technology. Specifically, this work documents current threats to agriculture (climate change, invasive pests, diseases, and costs) and how robotics and AI can act as countermeasures to deal with such threats. Finally, specific case studies and the application of intelligent robotic systems to them are analyzed, and the architecture for our intelligent decision system is proposed.

The occurring growth in e-commerce comes along with an increasing number of first-time delivery failures due to the customer’s absence at the delivery location. Failed deliveries result in rework, causing a significant impact on the carriers’ delivery cost. Hence, the last mile is the portion of a journey that involves moving people and commodities from a transportation hub to a final destination, which should be an efficient process. The above-mentioned concept is used in supply chain management and transportation planning. The paper at hand is a position paper that aims to scrutinize the concept of driverless last-mile delivery, with autonomous vehicles, in order to highlight and stress the challenges and limitations in the existing technology that hinder level five autonomous driving. Specifically, this work documents the current capabilities of the existing autonomous vehicles’ perception and cognition system and outlines their future skills towards addressing complete autonomous last-mile delivery, as well as efficient robotic process automation in logistics from warehouse/distribution center to hub’s delivery.

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion.