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Purpose Circumstances that are have a significant impact on it. In particular, environmental sustainability related to the increase of worldwide population, and market demand for agricultural products (with consumers more and more aware about cultivation and breeding techniques and interested in healthy and high-quality products) represent two of the key challenges that the agricultural sector is going to face in next years. In such a landscape, technological innovations that can support organizations and entrepreneurs to face these problems become increasingly important, and Industry 4.0 is the most striking example. Indeed, the Industry 4.0 paradigm aims to integrate digital technologies into business processes to raise productivity levels and to develop new business models. Accordingly, digital technologies play a similar role in the precision agriculture domain, and the purpose of this paper is to understand if the technologies at the basis of these two paradigms are the same or not. Design/methodology/approach The present work investigates how the two domains of Industry 4.0 and precision agriculture are connected to one another by analyzing the most used technologies in both the fields in order to highlight common patterns and technological overlaps. To reach such goal, an approach combining manual and automated analysis was developed. Findings The research work generated three main results: a dictionary of precision agriculture technologies including 324 terms; a graph, describing the connections between the technologies composing the dictionary; and a representation of the main technological clusters identified. Originality/value These show how the two domains under analysis are directly connected and describe the most important technologies to leverage when approaching digital transformation processes in the agricultural sector.

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The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.