Machine learning-enabled soil classification for precision agriculture: a study on spectral analysis and soil property determination

Abstract

Surface soil type classification is essential to enhance food production in precision farming. However, soil classification is time-consuming, laborious, and costly through the traditional methods. Recently, artificial intelligence-based methods, especially machine learning, have played a vigorous role in soil classification and its mapping. However, machine learning still makes exterior soil type classification and its mapping difficult due to various features and spatio-temporal inconsistencies. Therefore, the present study has tried to determine soil properties and sort accordingly using hyperspectral datasets and machine learning methods. We used field spectra generated by ASD Field Spec 4 device and satellite image. The proposed approach has identified three prominent soil types, Regur soil, Lateritic soil, and sand dunes according to soil taxonomy, with more than 95% success rate using satellite hyperspectral image and machine learning models. Thus, the outcome of the present study can be effectively utilized in healthy agricultural practices to increase global food production. In addition, the suggested strategy can be used in precision agriculture and environmental management.