MACHINE LEARNING BASED IRRIGATION SCHEDULING FOR SMART FARMING SYSTEMS

Abstract

This research focuses on developing an intelligent irrigation scheduling system using machine learning techniques to optimize water use in agriculture. Traditional irrigation systems often suffer from inefficiencies such as over- or under-irrigation, labor intensiveness, and lack of precision. To overcome these challenges, the project leverages real-time environmental data, including soil moisture, temperature, and crop type, to predict the optimal times for activating irrigation pumps. The primary goal of the project is to address the inefficiency and inaccuracy of traditional irrigation scheduling methods. By integrating machine learning into irrigation management, the system aims to reduce water waste, enhance crop health, and minimize labor requirements. The motivation for this project stems from the urgent need to optimize water use in agriculture, given increasing water scarcity and the impact of climate change. The proposed system comprises several key components. Firstly, data collection sensors gather information on soil moisture, temperature, and crop type, which is then preprocessed for model training. Machine learning models, including Bernoulli Naive Bayes and Ridge Classifier, are trained on historical data to predict irrigation needs. These models are evaluated using performance metrics, and the best-performing model is used to make real-time predictions. Finally, the system integrates with irrigation infrastructure to automate pump control based on model predictions