A self-calibration algorithm for soil moisture sensors using deep learning

Abstract

In the current era of smart agriculture, accurately measuring soil moisture has become crucial for optimising irrigation systems, significantly improving water use efficiency and crop yields. However, existing soil moisture sensor technologies often suffer from accuracy issues, leading to inefficient irrigation practices. The calibration of these sensors is limited by conventional methods that rely on extensive ground reference data, making the process both costly and impractical. This study introduces an innovative self-calibration method for soil moisture sensors using deep learning. The proposed method focuses on a novel strategy requiring only two characteristic points for calibration: saturation and field capacity. Deep learning algorithms enable effective and accurate in-situ self-calibration of sensors. This method was tested using a large dataset of simulated erroneous sensor readings generated with simulation software. The results demonstrate that the method significantly improves soil moisture measurement accuracy, with 84.83% of sensors showing improvement, offering a more agile and cost-effective implementation compared to traditional approaches. This advance represents a significant step towards more efficient and sustainable agriculture, offering farmers a valuable tool for optimal water and crop management, while highlighting the potential of deep learning in solving complex engineering challenges.