Soil health Prediction for Precision Agriculture using Machine Learning and Deep Learning

Incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in farming offers enormous potential for improving how soil health is being monitored and practiced in Precision Agriculture. In this regard, the focus of this paper is to provide a review on the rapidly progressing techniques which are AI/ML-based for prediction and monitoring of soil properties such as pH, moisture content, nitrogen, phosphorus, potassium, and organic carbon. This study analyzes 24 research articles published in the 2018–2023 period, classifying works by their methodologies, which include decision trees, random forests, and even deep learning models, assessing performance, data needs, and practicality. The study’s conclusions reflect the promise of AI and ML in improving the accuracy and efficiency of traditional methods of soil analysis. Additionally, the study identifies important gaps like data insufficiency, model applicability, and need for strong and simple framework that can be used for easy understanding by farmers. This review is written to assist in the formulation of strategies that combine innovation with agricultural sustainability to solidify AI research concerning soil health and agri-food system security.