This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.