TRANSFER LEARNING-ENABLED SMART IRRIGATION SYSTEM FOR OPTIMIZED WATER MANAGEMENT IN IOT- DRIVEN AGRICULTURAL NETWORK

Abstract

Internet of Things (IoT) -driven sensor networks, smart irrigation systems have drawn a lot of interest lately as a way to improve managing water in agriculture. The ineffective control mechanisms accompanying conventional irrigation technologies frequently result in water waste. Promising solutions to this problem include integrating cutting-edge artificial intelligence technologies like transfer learning. By utilizing pre-trained information from related domains, transferable learning enables the model to greatly improve performance and shorten its training period when deployed to new contexts. In this paper, we demonstrate a transferable training-enabled intelligent watering system that tracks the condition of crops, the environment, and moisture in the soil using IoT sensors. The framework may be used in various farming setups since it can adjust to changing circumstances in the environment. We aim to maximize crop health and minimize water waste by offering real-time irrigation suggestions derived from sensor data analysis. The results show that productivity in agriculture and water usage effectiveness have improved, and the system outperforms conventional techniques in terms of accuracy and flexibility