Using Long Short-Term Memory for Building Outdoor Agricultural Machinery

Chien-Hung Wu; Chun-Yi Lu; Jun-We Zhan; Hsin-Te Wu

doi:10.3389/fnbot.2020.00027

Using Long Short-Term Memory for Building Outdoor Agricultural Machinery

Front Neurorobot. 2020 May 29:14:27. doi: 10.3389/fnbot.2020.00027. eCollection 2020.

Authors

Chien-Hung Wu¹, Chun-Yi Lu², Jun-We Zhan³, Hsin-Te Wu⁴

Affiliations

¹ Department of Marine Recreation, National Penghu University of Science and Technology, Magong, Taiwan.
² Department of Information Management, National Penghu University of Science and Technology, Magong, Taiwan.
³ Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong, Taiwan.
⁴ Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan.

Abstract

Today, climate change has caused a decrease in agricultural output or overall yields that are not as expected; however, with the ongoing population explosion, many undeveloped countries have transformed into emerging countries and have transformed farmland to be used in other types of applications. The resulting decline in agricultural output further increases the severity of the food crisis. In this context, this study proposes an outdoor agricultural robot that uses Long Short-Term Memory (LSTM). The key features of this innovation include: (1) the robot is portable, and it uses green power to reduce installation cost, (2) the system combines the current environment with weather forecasts through LSTM to predict the correct timing for watering, (3) detecting the environment and utilizing information from weather forecasts can help the system to ensure that growing conditions are suitable for the crops, and (4) the robot is mainly for outdoor applications because such farms lack sufficient electricity and water resources, which makes the robot critical for environmental control and resource allocation. The experimental results indicate that the robot developed in this study can detect the environment effectively to control electricity and water resources. Additionally, because the system is planned to increase agricultural output significantly, the study predicts the variables through multivariate LSTM, which controls the power supply from the solar power system.

Keywords: artificial intelligence; automation equipment; deep learning; intelligent agriculture; long short-term memory; robot.