Applications of Raspberry Pi for Precision Agriculture—A Systematic Review

Astina Joice, Talha Tufaique, Humeera Tazeen, C. Igathinathane (), Zhao Zhang, Craig Whippo, John Hendrickson and David Archer
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Astina Joice: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
Talha Tufaique: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
Humeera Tazeen: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
C. Igathinathane: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
Zhao Zhang: Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China
Craig Whippo: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
John Hendrickson: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
David Archer: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA

Agriculture, 2025, vol. 15, issue 3, 1-31

Abstract: Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses this gap. However, information on Raspberry Pi’s use in PA remains limited. This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE Xplore ) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). Narrative synthesis and subgroup analysis were used to synthesize the results. Findings suggest Raspberry Pi can be a central unit to control sensors, enabling cost-effective automated decision support for PA, particularly in plant disease detection, site-specific weed management, plant phenotyping, biomass estimation, and irrigation systems. Despite focusing on these areas, further research is essential on other PA applications such as livestock monitoring, UAV-based applications, and farm management software. Additionally, Raspberry Pi can be used as a valuable learning tool for students, researchers, and farmers and can promote PA adoption globally, helping stakeholders realize its potential.

Keywords: agricultural automation; digital agriculture; edge device; infield measurement; machine learning; Raspberry Pi; systematic literature review (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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