Advancements in Soil Quality Assessment: A Comprehensive Review of Machine Learning and AI-Driven Approaches for Nutrient Deficiency Analysis

ABSTRACT Soil is an important resource worldwide with diverse physical, chemical, and biological properties. These properties vary from place to place because ecological variables such as temperature, moisture, and land use vary across different ecosystems. Soil quality has declined, which has led to increased demand for food, which poses significant problems in enhancing agricultural production and promoting environmental sustainability. The traditional methods for analyzing soil nutrients are labor-intensive, tedious, and expensive. The soil properties were effectively analyzed via artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) applications, to explain challenging problems with high accuracy and robustness. To interpret multidimensional data inputs derived from agro-industries and provide farmers with relevant information about crop conditions and soil management. AI can increase crop production by optimizing soil nutrient management. With artificial intelligence technology, farmers can identify potential deficits in soil quality, while Machine learning technologies, such as random forests (RF), support vector machines (SVMs), and Artificial and Deep neural networks (ANN, DNN), were used to generate predictive models on the basis of available soil data and auxiliary ecological variables. This review provides a detailed overview of the diverse AI tools and models used for the detection of various soil properties.