In the evolving landscape of precision agriculture, the integration of remote pest traps with deep learning technologies marks a critical step forward in remote pest detection, with the potential to substantially improve traditional pest monitoring methods. This article provides a comprehensive review of the developments, challenges, and innovative solutions in creating sensor-based electronic traps and applying deep learning for efficient and autonomous pest identification. By addressing the complexities of sensor integration, data collection, and the need for adaptive algorithms capable of classifying a wide range of insect pests, this review highlights the effective combination of electronic trap advancements with the precision offered by convolutional neural networks. An in-depth analysis of the technological advancements in electronic pest trap development is presented, highlighting improvements in design, efficiency, and sustainability while referring to ongoing and future challenges. Moreover, this article explores deep learning techniques, emphasizing on dataset enhancement and model optimization to overcome traditional challenges such as data scarcity and to improve the robustness of pest detection models. A thorough evaluation of various trap types against 85 unique pests is conducted, with the delta trap emerging as the most versatile, showcasing compatibility with multiple sensors and effectiveness against various pests. This review equips researchers, practitioners, and agricultural developers with critical insights and methodologies that can significantly enhance pest monitoring efficiency, reduce pesticide usage, and support sustainable agricultural practices.

This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method ($ReSuMe$), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of $83.4\%$, demonstrating the approach’s efficacy in precise movement activity classification. This method’s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs’ superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

Precision viticulture (PV) represents a specialized branch of precision agriculture, focusing on the application of advanced technologies, such as recent advances in electronics, artificial intelligence (AI), and automation, to make informed decisions that improve land productivity and crop quality and optimize vineyard operations. Through real-time sensing, predictive analytics, and automated intervention, PV enhances both the efficiency and sustainability of grape production. By integrating predictive analytics into the field of viticulture, real-time monitoring, timely decision-making, and intervention are enabled, improving resource management efficiency while also improving grapevine yield and quality. This article provides an in-depth review of recent advances in PV, focusing on the role of AI-driven analytics, sensing, and automation in vineyard management. In addition, emerging trends and challenges in the field are presented, highlighting their potential to drive sustainable and high-efficiency agricultural practices.

Graphene has long been considered a revolutionary material for the field of electronics due to its remarkable set of electronic properties, standing as a very promising candidate for the post-silicon era. However, it is not just a silicon replacement, but rather an enabling material for different computing paradigms. In this work, we investigate the use of graphene in devices and circuits that are employed for the realisation of computing architectures and systems. More specifically, we focus on impactful key applications such as conventional computing and Boolean logic, high-radix computing and multi-valued logic, memristive devices and in-memory-computing, neuromorphic applications, quantum computing and photonics. Additionally, taking into consideration the state-of-the-art as well as the existing graphene-related challenges that are still present, this work attempts to assess the possible future development of graphene-based devices, circuits and systems in each of the aforementioned fields and to propose a coarse yet directive roadmap for the material's future in computing architectures.