Machine learning and remote sensing techniques applied to estimate soil indicators – Review

Abstract

• Machine learning to process remote sensing data to estimate soil quality indicators. • Use of unmanned aerial vehicle (UAV) and satellite platforms to estimate soil indicators. • Selection of machine learning algorithms for processing agro-industrial systems databases. • Model for estimating soil quality in agricultural systems at a local scale. The demand for food based on intensive agriculture has decreased soil quality, posing great challenges such as increasing agricultural productivity and promoting environmental sustainability. Thus, researchers have focused on developing models for estimating soil quality based on artificial intelligence techniques for the processing of multidimensional data from agro-industrial systems, which provide useful information for farmers about soil management and crop conditions. However, a model for the application of these new technologies in medium and low-scale agricultural systems has not been identified. Therefore, a review of recent studies of crop yield prediction based on the estimation of chemical, physical, and biological soil quality indicators (SQI), which incorporate different machine learning (ML) techniques to process data from remote sensing (RS) systems, is presented. The advantages and disadvantages are also analyzed for: SQI estimates at regional and local scale, spectral bands used for analysis of plowed soils (bare soils) of cultivation plots, selection of minimun data set (MDS), use of unmanned aerial vehicle (UAV) and satellite platforms, data pre-processing, and selection of ML algorithms for processing biological systems databases (agro-industrial systems). Finally, we present a model to help estimate soil quality in agricultural systems at a local scale, based on ML to process RS data, in the model the inputs to the ML unit come from four different class data sets (RS, SQI, environmental data and crop management data). Crop management uses the production of the ML unit to adjust agricultural management practices and therefore improve crop yield.