Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation.

In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in areas where tea cultivation is carried out at the micro-watershed scale and the predictability of soil quality using ANN were evaluated. According to the results, the soil quality indices of tea-growing areas were generally classified as "medium" between 55 and 70 %.

Different approaches to estimating soil properties for digital soil map integrated with machine learning and remote sensing techniques in a sub-humid ecosystem.

Today, data mining has become a relevant topic in digital soil mapping. In this current study, prediction of some soil properties and their spatial distribution were examined by machine learning algorithms (Support Vector Machine, Artificial Neural Network) using reflectance values of Triplesat satellite image bands in Vezirköprü district of Samsun province. The band data obtained from different wavelengths revealed positive correlations between the electrical conductivity and calcium carbonate equivalent contents of the soils.