Soil quality is an important factor affecting the productivity and sustainability of agricultural systems. However, soil quality is influenced by various environmental and anthropogenic factors, such as climate change, land use, management practices and pollution. The interconnections of these factors and their impact on soil structure, fertility and resilience have received considerable attention. However, despite significant advances in this field, many aspects remain to be elucidated. Therefore, it is necessary to monitor and assess soil quality and its changes over time and space. The factors involved in this process are difficult to analyze due to the complexity and non-linearity of the relationships between soil variables. Artificial neural networks (ANN) are a type of machine learning method that can model these relationships with high accuracy and flexibility. This study proposes an intelligent prediction system through the construction of a neural network and a learning base to predict the effect of each factor on soil quality. The application of artificial neural networks to analyze soil quality factors has proven to be an effective and innovative approach. The established correspondence equation offers a nuanced understanding of the relationships between input variables and soil quality outcomes. This study also discusses the advantages and limitations of these methods and suggests some future directions for research.

Keywords: Soil quality, Artificial Neural Networks, Prediction, Factors, Remote Sensing