Microbial Soil Health Management Systems Using AI

Abstract

Soil health plays a pivotal role in sustainable agriculture, influencing crop productivity and ecosystem stability. However, traditional methods of soil management often lack the precision and adaptability needed to address the dynamic nature of soil environments. This study proposes an AI-driven microbial soil health management system designed to optimize soil conditions through the analysis of microbial diversity, nutrient availability, and environmental factors. The system utilizes machine learning algorithms, such as Random Forest (RF), Support Vector Machines (SVM), and Deep Learning (DL) models, to predict microbial activity and identify patterns in soil health based on real-time data from IoT sensors and remote sensing technology. In a case study involving various agricultural ecosystems, the system demonstrated a 25-30% improvement in soil health prediction accuracy compared to conventional methods. Additionally, the model successfully identified critical microbial species linked to soil fertility, enhancing nutrient cycling and promoting soil resilience. This research highlights the potential of AI in advancing microbial soil health management, offering a scalable, data-driven solution that enhances soil sustainability, optimizes agricultural productivity, and supports eco-friendly farming practices