Journal of Emerging Trends in Artificial Intelligence

ABSTRACT

This study designed, developed, and evaluated a decentralized AI-powered labor matching platform for the agro-industrial workforce of Meme Division, Southwest Cameroon a region where the Anglophone Crisis and plantation sector restructuring have fractured traditional labor recruitment networks. A concurrent mixed-methods design was employed across 8 agro-industrial plantations (rubber, oil palm, banana) in Kumba, Ekondo-Titi, and Mbonge, tracking 300 workers (150 women, 150 men) over eight months. Results revealed that voice-enabled, offline-capable platforms were significantly more accessible than text-based alternatives: 71% preferred voice interfaces and 88% required offline capability. Platform users experienced significant improvements: +0.9 additional days worked weekly, +2,200 CFA higher wages, and reduced wasted travel by 21 percentage points (p<0.001). Plantations also benefited through reduced labor shortages (-1.7 days monthly) and decreased harvest waste (-6.5 percentage points). The study concludes that worker-centric, decentralized platforms can reduce information asymmetry when designed with voice interfaces, offline capability, integration with informal networks, and blockchain-verified worker credentials.

Keywords: Artificial Intelligence; labor Matching Platforms; Agro-Industrial Workers; Meme Division Cameroon; Decentralized Platforms; Post-Conflict Agriculture