Hybrid ABC-WOA based Machine Learning Approach for Smart Irrigation System

An important development in contemporary farming methods is the smart irrigation system, which integrates technology to maximize crop productivity and conserve water. Utilizing state-of-the-art technologies like sensors, data analytics, and machine learning algorithms, smart irrigation systems revolutionize traditional farming methods this study presents a novel approach to irrigation prediction that makes use of machine learning algorithms to process temperature and moisture data from the soil. The study's goal was to provide a flexible, reasonably priced autonomous irrigation system for intelligent agricultural management. Finding out how successfully various models predicted the ideal times to irrigate a soil given its features involved assessing and training them using collected data. These models included KNN, Logistic Regression, Neural Networks, SVM, and Naïve Bayes. After a thorough examination, the Support Vector Machine (SVM) model continuously performed better in terms of sensitivity, specificity, and accuracy metrics, with scores of 97.29%, 98.17%, and 97.46%, respectively. Due to its strong performance, SVM is now the most trustworthy predictor for irrigation management, particularly when it comes to controlling pumping operations in response to temperature and soil moisture fluctuations. This method uses real-time environmental data to optimize irrigation systems, which should lead to improvements in resource efficiency and production in sustainable agriculture practices.