Precision agriculture leverages IoT sensors to monitor soil health and optimise crop yields in agricultural settings. Data from field trials was analysed using linear regression models to assess the relationship between sensor readings and yield outcomes. A comparative analysis revealed that IoT sensors significantly improved soil moisture levels by an average of 15% in Burkina Faso, leading to a 7.2% increase in maize yields compared to conventional farming methods. The study underscores the potential of IoT sensors for enhancing crop productivity through precise data-driven agricultural practices. Investment should be prioritised in IoT sensor deployment and training programmes for local farmers to maximise yield improvements. Precision Agriculture, Soil Health, Crop Yields, IoT Sensors, Burkina Faso, Southern Sudan Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.