Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Abstract

PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

Characterisation and causal model of the holistic dynamics of the integral sustainability of the agri-food system

“…Technology is of major importance [ 49 ] due to its implication for the development of activities that trigger major consequences in the events that are executed in the sector under study that put their sustainability at risk. Furthermore, the processes leave traces that alter the life cycles within a region if they are not studied in a systemic way, whereby their effects in the environment where they develop, such a population and the very existence of the process are put at risk.…”

“…The tools available for the application of systems that are linked to the circular economy are largely aimed at major companies, and exclude SMEs, which to a lesser extent, are also aware of the need for the transformation of their production systems regarding the general welfare of society, the economy of the regions, and the conservation of the environment. However, Industry 4.0 is already offering sustainable practices focused on the circular economy in these organisations [ 48 , 49 ].…”

“…On the other hand, PLM contributes towards the transition of Industry 4.0 from the digitalisation of processes to better control, by monitoring via good traceability and follow-up practices for better decision making. In addition to Industry 4.0 tools, such as artificial intelligence, IoT, machine learning, and big data, PLM should be integrated into the agri-food sector and precision agriculture to optimise productivity and resource efficiency [ 49 , 58 – 60 ].…”