Scientists developed new models using a low-cost sensor to measure soil moisture and pH levels accurately. By analyzing soil resistivity, they created regression models for different soil types, considering temperature and sensor depth. The models were highly accurate, with a 98% success rate. This method offers a cost-effective way to monitor soil conditions for agriculture, meteorology, and horticulture purposes.