Efficient Treeline Irrigation System Using Machine Learning Algorithm

The goal of the study is to develop an efficient irrigation system using the sensors and machine learning to give the right amount of water to each tree along the highway. It uses temperature and soil moisture sensors to collect data from the surroundings of the trees. A microcontroller processes this data and controls a valve to send the correct amount of water using a solenoid valve. The system uses logistic regression, a machine learning method, to decide when and how much water to give. The data is stored locally, helping us to analyze and make decisions about watering. This system checks if a tree needs water or not. Compared to traditional irrigation systems, the system uses 40% less water. It constantly monitors the soil moisture and temperature in real-time to decide when to water a tree. If the soil moisture is below 40%, up to 2 liters of water is given. If the temperature is between 27°C and 30°C, slightly less water is given. When the temperature is above 30°C, the tree gets more water. The significance value is 0.0001. The proposed efficient irrigation system confirms better water management for highway trees. This reduces water usage and manual labor and promotes a green environment. This study shows how technologies can help global problems like water scarcity and plant care. It gives a sustainable and economical solution for the future.