Industry 4.0 technologies in Smart Agriculture: A review and a Technology Assessment Model proposition

Deniz Uztürk and Gülçin Büyüközkan

Technological Forecasting and Social Change, 2024, vol. 208, issue C

Abstract: Amidst the growing need for sustainable evolution in the agricultural sector, this study explores the progressive integration of Industry 4.0 (I4.0) technologies to foster the transition towards Smart Agriculture (SA). We aim to elucidate the contemporary relationship between agriculture and I4.0, identify the most promising I4.0 technologies that cater to the unique challenges faced by farmers in SA, and establish a comprehensive assessment framework for evaluating and prioritizing these technologies for adoption. Utilizing the SPAR-4-SLR (Scientific Procedures and Rationales for SystematicLiterature Reviews) framework for a systematic literature review, we critically analyze the impact of IoT, Blockchain, Cloud Computing, Machine Learning, and Sensors etc. within the agricultural domain. Our research uncovers the crucial criteria and factors necessary for an effective technology assessment framework, designed to aid stakeholders in making informed decisions about I4.0 technology adoption. Although our review is limited to literature from the Scopus and Web of Science databases, focusing exclusively on Q1 and Q2 ranked articles, the findings offer a novel perspective on the synergistic evolution of agriculture and I4.0. The proposed framework integrates strategic, tactical, and operational aspects, guiding stakeholders through a qualitative evaluation process informed by extensive literature analysis and expert insights. Despite the limitations pertaining to database scope and qualitative focus, our study provides a foundational understanding of the technological advancements shaping SA and lays the groundwork for future research in this pivotal area.

Keywords: Deep Learning; Industry 4.0 Technologies; IoT; Machine Learning; Robotics; Smart Agriculture; Systematic Review (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004384

DOI: 10.1016/j.techfore.2024.123640

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