A deep reinforcement learning-based multi-agent area coverage control for smart agriculture. (July 2022)

• Record Type: Journal Article
• Authors: Din, Ahmad; Ismail, Muhammed Yousoof; Shah, Babar; Babar, Mohammad; Ali, Farman; Baig, Siddique Ullah
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Area coverage -- Smart sensors -- Deep reinforcement learning -- Smart agriculture -- Precision agriculture -- Multi-robotics systems -- Internet of agricultural things (IoAT); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108089
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Precision agriculture (PA) is a collage of strategies and technologies to optimize operations and decisions in farms by using spatial and temporal variabilities in yield, crops, and soil within an agricultural plot. It is a data-driven technique, therefore, selective treatment of crops and soil, and managing variabilities using robots and smart sensors is the next improvement in PA. In this paper, it is modeled as a multi-agent patrolling problem, where robots visit subregions that required immediate attention in the agricultural field. Furthermore, for area coverage / patrolling task in the agricultural plot, a centralized Convolutional Neural Network (CNN) based Dual Deep Q-learning (DDQN) is proposed. A customized reward function is designed, which rewards worth-visiting idle regions, and punishes undesirable actions. A proposed algorithm has been compared with various algorithms including individual Q-learning (IRL), uniform coverage (UC), and Behavior-Based Robotics coverage (BBR) for different scenarios in the agricultural plots.