An Intelligent and Cost-Effective IoT-Based Irrigation System Using Machine Learning

This work presents and implements a low-cost irrigation system for smart agriculture that is based on the Internet of Things (IoT). In order to continuously monitor environmental data in real time, the system is equipped with a network of sensors, including pressure, temperature, moisture, and water level sensors. In order to anticipate when irrigation pumps will switch on, machine learning techniques including Artificial Neural Networks (ANN), Decision Trees (DT), Naive Bayes (NB), and Support Vector Machines (SVM) are linked depending on established parameters. The study shows that the ANN model can identify complicated patterns in the agricultural environment with an accuracy of up to 98.33%. Farmers are able to make well-informed decisions quickly thanks to the cloud connectivity and intuitive interface of remote monitoring and control. Because predictive modeling minimizes pump activation delays, it lessens the chance of both over- and under-irrigation. The suggested strategy makes the most use of available water and provides opportunities for precision farming, which is a major step forward for sustainable agriculture. The study's findings demonstrate how well the system uses resources and open the door for the future creation of innovative, scalable agricultural technology.