IoT-Driven Soil Nutrient Measurement Using LoRa and Broken-Stick Regression Techniques

Soil nutrient measurement systems are crucial for optimizing crop growth and yield in agriculture. Nitrogen (N), Phosphorus (P), and Potassium (K) are vital for crop growth. Many measuring systems have been presented, but accurately measuring soil nutrients is a challenging issue for efficient agriculture and environmental management. Conventional methods are often time-consuming, expensive, and incorrect due to soil variability and other factors. To address this issue, this study introduces Broken-Stick Regressive LoRa Technology (BSRLRT), aimed at accurately measuring soil nutrients based on IoT technology through sensor node deployment and data collection from a farmland. Initially, the broken-stick regression method is used to analyze and identify the exact position of the sensor nodes for efficient data collection. In addition, Long Range (LoRa) technology is used to monitor and collect data. The proposed method was experimentally deployed on V.C. Farm, Mandya, India.