Soil health monitoring is important for the general growth of any plant or crop and is a primary requirement for precision agriculture and geoscientific studies. Properties of the soil such as moisture, temperature, and NPK nutrients (Nitrogen, Phosphorus, and Potassium) are crucial for understanding the health of the soil for crops. Traditional methods for soil health determination depend on on-field soil sampling and lab analysis, which is tedious and time-consuming. To this end, this thesis develops the design and implements the FOCUS (Fast Observation and Collection of Soil) bot which is an all-terrain robotic vehicle capable of autonomously navigating a predetermined terrain, choosing locations to collect data, drilling holes into the ground, and collecting soil moisture, temperature, nitrogen, phosphorus, and potassium data. As a complementary study, this thesis presents data analysis information on how the sampling of soil parameters in the field can be optimized using software-based interpolation and estimation methods by leveraging Microsoft's Farmvibes AI pipeline. The FOCUS bot is the first version of the field-deployable robot and is user-friendly and ensures data collection with minimal setup and technical expertise.