As the demand for irrigation rises due to population growth, the need for precision irrigation and accurate soil moisture measurement becomes increasingly critical.

The study established a strong linear relationship between apparent dielectric permittivity and volumetric water content across various soil types and salinity levels.

Optimal soil moisture is crucial for plant growth, influencing nutrient uptake, photosynthesis, and transpiration.

Insufficient soil moisture can reduce crop yields, while excessive moisture can lead to root diseases and increased greenhouse gas emissions, particularly nitrous oxide (N2O), which is 300 times more potent than carbon dioxide.

Effective irrigation management is complicated by factors such as soil type, climate, and crop needs, with agriculture accounting for roughly 70% of global freshwater withdrawals.

The addition of the ADS1115 notably improved the accuracy and precision of low-cost sensors, reducing measurement fluctuations and increasing R2 values.

A thorough calibration process was implemented using a gravimetric method to measure soil moisture loss at controlled temperatures.

Calibration results indicated that polynomial equations provided a reliable fit for soil water content, achieving R2 values greater than 0.98.

Conducted at the Costa Rican Institute of Technology, this study evaluated various soil moisture sensors, including low-cost resistive and capacitive sensors, the intermediate VH400, and the high-cost 5TM sensor.

The research specifically aimed to assess the multivariate calibration model of Kargas and Soulis against traditional calibration methods using the WET sensor.

Findings revealed that the multivariate model significantly enhanced volumetric water content estimations compared to default calibration methods.

Among the sensors tested, the VH400 and 5TM provided more accurate and consistent readings than the low-cost sensors, particularly when paired with an external 16-bit analog-to-digital converter (ADS1115).