Background: Precision Agriculture (PA) technologies offer immense potential for optimizing input use and increasing farm profitability. However, adoption among smallholder farmers in developing regions remains disproportionately low.

Objective: This study investigates the economic viability of basic PA technologies (soil sensors and drone imagery) and identifies the primary barriers to their adoption by smallholder farmers.

Method: This study uses a simulated dataset created for academic training purposes. A simulated survey of 200 smallholder farmers was generated. A Cost-Benefit Analysis (CBA) was conducted to determine profitability, and a binary Logit regression model was estimated using STATA to identify socio-economic barriers to adoption.

Key Results: The CBA revealed a positive Net Present Value (NPV) and a Benefit-Cost Ratio (BCR) of 1.85 for PA adopters, indicating economic viability. However, the Logit model identified high initial capital costs (p<0.01) and lack of technical training (p<0.05) as significant deterrents to adoption.