Intelligent Farming System: IoT and Adaptive Machine Learning for Sustainable Agriculture

The combination of the Internet of Things (IoT) and Adaptive Machine Learning (AML) presents significant opportunities for precision agriculture by allowing real-time observation, flexible decision-making, and forecasting analyses. This study explores the development of an adaptive ML model for smart agriculture systems, focusing on efficient pest detection and predictive yield analysis. The suggested models intend to enhance resource efficiency and minimize environmental effects by incorporating diverse data sources such as IoT-enabled sensors, satellite images, and past agricultural records. The research highlights the design of scalable, interpretable, and cost-effective ML algorithms tailored for diverse agricultural landscapes. These systems address critical challenges such as real-time data processing, pesticide optimization, and ethical concerns related to data privacy. The findings contribute to advancing sustainable and intelligent farming practices, providing a foundation for future innovations in precision agriculture.