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.

如今，气候变化已导致农业产出或总产下降，达不到预期。但是，随着人口激增，许多不发达国家已转变为新兴国家，并将农田转化为其他类型的应用。农业产量的下降进一步加剧了粮食危机的严重性。在这种情况下，这项研究提出了一种使用长短期记忆（LSTM）的户外农业机器人。这项创新的关键特征包括：（1）机器人是便携式的，并且使用绿色电源来降低安装成本；（2）系统将当前环境与通过LSTM进行的天气预报相结合，以预测浇水的正确时间；（3）检测环境并利用天气预报中的信息可以帮助系统确保生长条件适合农作物，并且（4）机器人主要用于室外应用，因为此类农场缺乏足够的电力和水资源，这使得对环境控制和资源分配至关重要的机器人。实验结果表明，本研究开发的机器人可以有效地检测环境，以控制电力和水资源。此外，由于计划将该系统显着增加农业产量，因此该研究通过多元LSTM预测了变量，该变量控制了太阳能系统的电力供应。（4）该机器人主要用于室外应用，因为此类农场缺乏足够的电力和水资源，这使得该机器人对于环境控制和资源分配至关重要。实验结果表明，本研究开发的机器人可以有效地检测环境，以控制电力和水资源。此外，由于计划将该系统显着增加农业产量，因此该研究通过多元LSTM预测了变量，该变量控制了太阳能系统的电力供应。（4）该机器人主要用于室外应用，因为此类农场缺乏足够的电力和水资源，这使得该机器人对于环境控制和资源分配至关重要。实验结果表明，本研究开发的机器人可以有效地检测环境，以控制电力和水资源。此外，由于计划将该系统显着增加农业产量，因此该研究通过多元LSTM预测了变量，该变量控制了太阳能系统的电力供应。