Artificial Intelligent Techniques for Wireless Communication and Networking

4An Empirical Study of Crop Yield Prediction Using Reinforcement Learning

M. P. Vaishnnave^1* and R. Manivannan²

¹ Dept of IT, University College of Engineering Villupuram,Villpuram, India

² Dept of CSE, Meenakshi Ramaswamy Engineering College, Ariyalur, India

Abstract

In the world economy, agriculture plays a vital role. Stresses on the agriculture system will increase as the human population continues to expand. With the advancement of modern research fields, agritechnology and accurate farming, which are also referred to as digital agriculture, employ data-intensive approaches to stimulate agricultural production, minimizing its environmental impact. Reinforcement Learning (RL) has grown with large data technologies and high-performance computing to build new possibilities to activate, calculate and recognize. In the agricultural crop forecast, we will present a comprehensive review. There are also several related papers which highlight the main features of popular RL models.

Keywords: Agriculture, crop prediction, deep learning, machine learning, reinforcement learning

4.1 Introduction

The science of training machines is commonly used, and not for anything to learn and create models for future predictions. In the global economy, farming plays a crucial role. As the human population continues to develop, recognizing global crop yields is central to addressing the challenges of food security and reducing the impacts of climate change. A significant agricultural issue is ...