EcoSprout: Machine Learning-Powered Smart Sprinkler System

Smart sprinkler systems employ machine learning to forecast soil moisture levels based on environmental parameters and optimize water usage. This novel system incorporates temperature, humidity, and other environmental data to assist users in determining when and how much to water. Sensors like humidity, temperature, and soil moisture sensors are used to collect data for the system. Machine learning approaches and supervised learning models such as regression, decision trees are used to forecast and plan correlations between environmental conditions and soil moisture levels for historical data sets. Smart sprinkler systems allow you to control the amount of water available on specific crops through specific irrigation schedules based on the needs and preferences of each crop. The system's machine learning algorithms can make data-driven irrigation decisions that deliver the optimal amount of soil moisture for each crop's growing cycle taking into account your crop's exact water requirements Provides yields and its quality is enhanced by watering the plants properly and maintaining adequate water levels and drainage or root rot problems of the poor Avoid Smart sprinklers adaptable to different fields of water the variability of irrigation that can result from over-irrigation is determined by its flexibility.