The Smart Agriculture Using IoT-Based Methods:

A Survey

Abstract

Agriculture faces major challenges, including climate change, resource depletion and increasing demand for food. Conventional agricultural methods struggle to cope with this pressure, which is often caused by inefficiencies and unsustainable practices. The combination of Internet of Things (IoT) and deep learning (DL) technologies offers promising solutions for the agriculture. This article explores three major challenges facing modern agriculture - precision irrigation, plant disease detection, and resource optimization - and explores how IoT and deep learning can be used to address these issues. The paper also explores how the deep learning architecture Long Short-Term Memory (LSTM) and CNN network can provide predictive capabilities, especially for time series data such as crop growth, weather, and resource use. By focusing on these key areas, we demonstrate how the synergy of IoT and deep learning can promote sustainable, efficient and productive agricultural operations.