Predictive analytics using artificial intelligence for soil degradation and fertility mapping

Deteriorating soil and decreasing fertility of the soil presents a major challenge to sustainable agriculture and global food security, especially in areas where intensive cropping usages combined with climate variability are causing land deterioration at an alarming rate. Artificial intelligence (AI) and predictive analytics are providing new avenues for the monitoring, modelling, and management of soil health - tools that can be more precise than traditional methods. The goal of this research is to investigate the potential of AI-based predictive analytics in detecting patterns for soil degradation and fertility mapping which are useful for better decision-making by farmers. The study was based on a quantitative research approach and employed a structured questionnaire which was administered to agricultural users of soils data. The data were processed and analyzed using descriptive statistics and multivariate methods to study the relationship between predictive accuracy, management of soil and AI application. The analysis shows that AI predictive models greatly contribute to the identification of early degradation, refine geographical estimation of fertility and form empirical science based farming. The implications of model reliability on data quality and multi-source integration are also discussed. The study concludes that the application of AI empowered predictive analytics has a high potential to reinforce soil monitoring systems and sustainable agricultural productivity in general, and offers an scalable framework for evidence based land resource management.