Artificial Intelligence and Machine Learning in Soil Analysis for Precision Agriculture: A Review

Abstract

Soil analysis is a crucial component of precision agriculture, helping to evaluate soil fitness, fertility, and productivity. Conventional soil testing methods, although accurate, are often time-consuming and labor-intensive. AI and ML hold significant promise in transforming traditional soil analysis into an efficient and scalable process, contributing to the global demand for sustainable and precision farming. Recent improvements in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized soil testing via providing rapid and specific assessments of soil homes consisting of pH, particle distribution, nutrient availability, and soil texture. AI-driven fashions, such as neural networks, support vector machines, and deep neural-to-know frameworks, have validated high accuracy in predicting soil parameters based on facts accrued from far-flung sensing, spectroscopy, and IoT-based sensors. This review discusses the integration of AI and ML in soil analysis, focusing on their role in improving data accuracy, reducing costs, and enabling large-scale soil monitoring. It highlights their potential to advance sustainable agriculture through optimized nutrient management, crop yield prediction, and improved land use planning. Future research should focus on enhancing AI models by incorporating real-time soil data, integrating diverse datasets, developing standardized methodologies, and inventing farmer-friendly devices to improve the accuracy of soil predictions and facilitate practical applications in the field.