What question did this study set out to answer?

To systematically evaluate the maturity and readiness of Agriculture 4.0 technologies in agricultural marketing.

January 21, 2026Open Access

The Validation–Deployment Gap in Agricultural Information Systems: A Systematic Technology Readiness Assessment

MAMary Elsy Arzuaga-OchoaUniversity of the Coast MAMelisa Acosta-CollUniversity of the Coast MBMauricio Barrios BarriosUniversity of the Coast

Key Points

To systematically evaluate the maturity and readiness of Agriculture 4.0 technologies in agricultural marketing.
Conducted a systematic literature review of 99 peer-reviewed studies (2019–2025) using PRISMA protocols.
Assessed algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs).
Identified dominant trends, including hybrid recommendation systems and blockchain applications.
Hybrid recommendation systems showed accuracies of 80–92%, while blockchain had fast transaction times (<2 seconds) with limited adoption.
Machine learning models achieved predictive accuracies of 85–95%, and IoT systems reported over 95% data transmission reliability.
77.8% of technologies remain at validation stages (TRL ≤ 5), with only 3% achieving operational deployment beyond one year.

Abstract

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions.

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