Towards Sustainable Irrigation

Abstract

Smart irrigation systems integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies are revolutionizing water management in agriculture. A typological classification is displayed covering classical algorithms (KNN, SVM, Random Forest), deep learning (CNN, LSTM), crossbreed models, reinforcement learning, and transfer learning approaches. These models are assessed based on expectation precision, water-saving execution, taking a toll, scalability, and versatility. The come-about appears that whereas classical models are cost-effective and simple to convey, profound learning and hybrid strategies offer prevalent exactness and vigor. Support learning and exchange learning to illustrate promising flexibility and asset optimization capabilities. Despite these advances, challenges remain in terms of sending fetched sensors with unwavering quality and versatility over assorted agrarian settings.