Comparative Study of Organic Farming using IoT and Machine Learning Techniques

Abstract

Agriculture is a fundamental industry that supports economic growth and sustains human life. However, the excessive use of chemical pesticides and fertilizers has led to severe health and environmental concerns, necessitating a shift toward organic farming. The integration of the Internet of Things (IoT) in agriculture has emerged as a transformative approach to enhance productivity, quality, and sustainability. IoT-based smart farming allows farmers to monitor real-time environmental parameters such as soil moisture and temperature at a minimal cost, enabling efficient decision-making and resource optimization. This research focuses on developing an IoT-based organic farming system that automates irrigation, recommends optimal vermicompost application, and ensures better crop growth through real-time data analysis. IoT comprises interconnected devices with sensors, cloud computing, and analytics that assist farmers in reducing manual labour while improving efficiency. The study highlights how IoT enhances agricultural practices by providing insights into soil fertility, water management, and environmental conditions, fostering a sustainable and eco-friendly farming ecosystem. By leveraging IoT, modern farming can mitigate the adverse effects of chemical usage while ensuring long-term agricultural sustainability.