A Review On Smart Agriculture System Using Machine Learning and IOT
Dr. Snehal Golait , Anshika Palewar, Snehal Bhagat, Om Gupta, Sujal Kothale
DOI : 10.46335/IJIES.2026.11.2.4
Abstract – Agriculture plays a vital role in the economy, but traditional farming methods lack efficiency and accuracy. This review paper analyses various machine learning and IoT-based approaches used for crop disease prediction and smart irrigation. The study highlights different algorithms, sensors, and communication technologies used in modern agriculture systems. The objective of this paper is to provide a comparative analysis of existing methods and identify research gaps for future work. In recent years, modern technologies such as Machine Learning and the Internet of Things have been used to improve agricultural practices. Machine learning techniques help in identifying crop diseases by analysing images of plant leaves. IoT-based systems use sensors to monitor soil moisture, temperature, and humidity in real time and control irrigation automatically. These technologies help farmers make better decisions and reduce manual effort.
