USE OF ARTIFICIAL NEURAL NETWORKS OF DIFFERENT ARCHITECTURE AND LEARNING RATE TO PREDICT SOIL HUMUS CONTENT USING NORMALIZED DIFFERENCE VEGETATION INDEX

Abstract

As far as remotely sensed normalized difference vegetation index (NDVI) applications extend to more specific subjects than plants vegetation conditions assessment, studies are conducted to adopt this spatial index for evaluation of soil properties, e.g., soil nutrients and organic matter content, electrical conductivity, pH, etc. [1–3]. Our studies are directed to the development of mathematical model for derivation of soil humus content using normalized difference vegetation index values. Although some success has been achieved in this field by the means of regression analysis, the prediction accuracy and model fitting quality are still insufficient to provide it for practical implementation [4]. As it is known that artificial neural networks (ANN) in many cases provide much better results than traditional regression analysis, the study was performed with different ANN architecture and learning rates to establish the relationship and improve the quality of soil humus content prediction based on the values of spatial vegetation index [5].