Evidence map›Paper›PMID 42313460›Full record

ReviewTheScientificWorldJournal2026

Artificial Intelligence in East African Agriculture: A Systematic Review of Applications, Adoption, and Implications for Smallholder Farmers.

Zebenay Shitaye, Manale Andargie Embyale, Tewodros Adane Nega, Getachew Eshetu Gidelew, Marta Mohammed

Abstract readReview
In one paragraph

Review in TheScientificWorldJournal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zebenay ShitayeDepartment of Rural Development and Agricultural Extension, College of Agriculture and Natural Resource, Debere Markos University, Debere Markos, Ethiopia, dmu.edu.et.ORCID https://orcid.org/0000-0003-1747-618X
Manale Andargie EmbyaleDepartment of agricultural Economics, College of Agriculture and Natural Resource, Debere Markos University, Debere Markos, Ethiopia, dmu.edu.et.
Tewodros Adane NegaDepartment of Agricultural Economics, College of Agriculture and Environmental Science, Debark University, Debark, Ethiopia, dku.edu.et.
Getachew Eshetu GidelewDepartment of Rural Development and Agricultural Extension, College of Agriculture, Wollo University, Dessie, Ethiopia, wu.edu.et.
Marta MohammedDepartment of Rural Development and Agricultural Extension, College of Agriculture and Natural Resource, Debere Markos University, Debere Markos, Ethiopia, dmu.edu.et.ORCID https://orcid.org/0009-0007-0069-0274

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has evolved over several decades and is increasingly recognized as a transformative tool for improving agricultural productivity, resilience, and access to information, particularly in smallholder farming systems such as those in East Africa. This systematic literature review synthesizes existing evidence on the applications, adoption dynamics, implications, and policy considerations of AI in East African agriculture over the period 1985-2025. The study follows PRISMA guidelines and draws on peer-reviewed articles, conference papers, and institutional reports retrieved from major academic databases, including Scopus, Web of Science, and Google Scholar. A thematic analysis approach was used to organize and interpret the findings. The review shows that early developments in AI-related agricultural technologies were limited and largely experimental, but advancements in digital technologies, mobile connectivity, remote sensing, and data analytics have significantly expanded AI applications in recent years. Key application areas identified include AI-powered advisory services, precision agriculture, crop and pest monitoring, financial and market intelligence, and climate-smart agriculture. These technologies support farmers by enabling real-time, data-driven decision-making, improving resource use efficiency, and enhancing access to agricultural information and markets. Despite these advancements, the adoption of AI among smallholder farmers in East Africa remains relatively low and uneven. The review identifies several factors influencing adoption, including education, digital literacy, access to extension services, infrastructure availability, income levels, and institutional support. Major barriers include limited rural infrastructure, high costs, inadequate digital skills, weak integration with extension systems, and data-related constraints. Although AI offers promising benefits in terms of productivity, information access, and inclusivity, concerns remain regarding digital inequality, affordability, data privacy, and potential exclusion of marginalized groups. From a policy perspective, the study underscores the importance of strengthening digital infrastructure, investing in capacity building, enhancing extension services, and promoting inclusive public-private partnerships to support the effective deployment of AI technologies. Overall, the review concludes that although AI has significant potential to transform East African agriculture, its impact depends on addressing systemic constraints and ensuring that technologies are accessible, affordable, and aligned with the needs of smallholder farmers. The study also identifies research gaps and suggests future directions for advancing AI integration in the region.

Indexed as

adoptionagricultural technologyartificial intelligenceclimate-smart agriculturedigital agricultureEast Africaprecision agriculturesmallholder farmers

Identifiers

PMID42313460
PMCPMC13277772

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.