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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #412436
Research Project: Transdisciplinary Research that Improves the Productivity and Sustainability of Northern Great Plains Agroecosystems and the Well-Being of the Communities They Serve

Location: Northern Great Plains Research Laboratory

Title: Applications of Raspberry Pi for Precision Agriculture — A Systematic Review

Author
item JOICE, ASTINA - North Dakota State University
item TUFAIQUE, TALHA - North Dakota State University
item TAZEEN, HUMEERA - North Dakota State University
item IGATHINATHANE, CANNAYEN - North Dakota State University
item ZHANG, ZHAO - China Agricultural University
item Whippo, Craig
item Hendrickson, John
item Archer, David

Submitted to: Agriculture
Publication Type: Literature Review
Publication Acceptance Date: 1/15/2025
Publication Date: 1/21/2025
Citation: Joice, A., Tufaique, T., Tazeen, H., Igathinathane, C., Zhang, Z., Whippo, C.W., Hendrickson, J.R., Archer, D.W. Applications of Raspberry Pi for Precision Agriculture — A Systematic Review. Agriculture. 2025, 15:227. https://doi.org/10.3390/agriculture15030227.
DOI: https://doi.org/10.3390/agriculture15030227

Interpretive Summary:

Technical 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.