Soil Health Monitoring With Iot And Machine Learning For Agroecological Management

Abstract

Soil health is a critical factor in ensuring sustainable agricultural practices, particularly in the context of agroecological management. Traditional soil monitoring techniques often suffer from inefficiency, lack of real-time data, and high operational costs. The integration of Internet of Things (IoT) and machine learning offers a promising solution to these challenges. This research explores the development and application of an IoT-based soil health monitoring system, coupled with machine learning algorithms, to provide real-time, data-driven insights into soil quality. By utilizing a network of sensors to collect parameters such as soil moisture, pH, temperature, and nutrient content, the system enables continuous monitoring and analysis. Machine learning models are applied to predict soil health trends, optimize resource allocation, and improve decision-making for sustainable farming practices. The results demonstrate the system’s potential to enhance agroecological management by facilitating informed decisions that promote soil health, reduce input costs, and support sustainable farming practices. The paper also highlights the challenges faced in the deployment of such technologies, including sensor calibration, data accuracy, and integration with existing agricultural systems, as well as the potential for future advancements.