Open Access

ARTICLE

Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

1 Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan
2 Department of Informatics Management, Politeknik Negeri Sriwijaya, Palembang, 30139, Indonesia
3 Department of Information Technology, Takming University of Science and Technology, Taipei City, 11451, Taiwan

* Corresponding Author: Sung-Jung Hsiao. Email:

Computers, Materials & Continua 2024, 80(3), 3733-3759. https://doi.org/10.32604/cmc.2024.053634

Received 06 May 2024; Accepted 26 July 2024; Issue published 12 September 2024

Abstract

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system. A value function-based will be employed to perform DRL model called deep Q-network (DQN) which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior. The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%. Meanwhile, the results of comparative accuracy model experiments of the proposed DQN, individual Q-learning, uniform coverage (UC), and Naïve Bayes classifier (NBC) are 97.60%, 95.30%, 96.50%, and 92.30%, respectively. From the results of the comparative experiment, it can be seen that the proposed DQN used in the study has the most optimal accuracy. Testing with 22 test scenarios for “optimal,” “sub-optimal,” and “not-optimal” conditions was carried out to ensure the system runs well in the real-world data. The accuracy percentage which is generated from the real-world data reaches 95.45%. From the results of the cost analysis, the system can provide a low-cost system compared to the conventional system.

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Keywords

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