Smart Agriculture: Integrating IoT and Machine Learning for Precision Farming

Abstract

Precision farming, often known as smart agriculture, has become a revolutionary method for updating and improving agricultural techniques. This abstract captures the core of a proposed system that combines machine learning and the Internet of Things (IoT) for precision farming. This system intends to transform agriculture by improving productivity, resource efficiency, and sustainability via the seamless integration of real-time data collecting from IoT sensors with the analytical capabilities of machine learning algorithms. The components of the suggested system include IoT sensors and tools for weather, crop, and livestock monitoring. A centralized cloud platform that acts as a storehouse for real-time data receives data from these sensors. These data are processed by machine learning algorithms, which also provide individualized crop suggestions, early pest and disease detection, and optimal watering schedules. Proactive interventions, such automatic irrigation, are made possible through automation and control systems. Farmers and agronomists can make educated decisions because of the accessible information, suggestions, and visualizations offered by user-friendly dashboards and mobile apps. This integrated system offers a wide range of advantages, such as higher agricultural yields, better crop quality, less resource waste, fewer operating costs, greater sustainability, and risk reduction. However, there are issues that must be resolved about early investments, data protection, instruction, and connection. Despite these difficulties, implementing this integrated system for precision farming has significant potential benefits. It provides a possible route for tackling issues with global food security, resource conservation, and agriculture's environmental impact. Precision farming is an essential strategy to fulfill the ever-increasing needs of a fast rising global population as technology develops.