The RapidMapper$state-of-the-art in mobile proximal soil sensing based on a novel multi-sensor platform

Abstract

A rapid, accurate and high-resolution characterization of soil properties is essential for a successful implementation of site-specific soil and crop management in precision agriculture. In recent years, proximal soil sensors have been developed as efficient tools for high-resolution soil mapping. Nevertheless, no single soil sensor is capable of accurately quantifying all agronomically relevant soil properties. By integrating multiple proximal sensing technologies in a single multi-sensor platform, measuring simultaneously and fusing the sensor data by applying state-of-the-art machine learning algorithms, a greater variety of soil properties can be determined and the performance of soil property estimations can be improved. To this end, a multi-sensor platform for on-the-go topsoil mapping called RapidMapper was developed. Currently, it integrates near infrared and gamma-ray spectroscopy, apparent electrical conductivity and pH potentiometry (ion-selective electrode) sensors. We evaluated RapidMapper’s functionality across various plots and fields in different regions of Germany. This paper focuses on data quality assessment for one multi-plot sensor test site and one agricultural field in northeast Germany. This includes analyzing sensor response across different soils, evaluating measurement consistency over three years on the same field and comparing data with other platforms and against laboratory measurements. The evaluation demonstrated RapidMapper’s consistent mapping capabilities, high temporal consistency and strong agreement with similar devices.