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.
Highlights
The world is currently facing energy and food crises; many countries are transforming farmland into industrial land for related usage because they are transforming from being undeveloped to being emerging countries
The machinery connects with the sensors and the development board through Internet of Things (IoT); there are various types of sensors, such as barometric pressure and light sensors on the machinery, as well as a mini pumping motor, a 3-in-1 soil tester, and a solar power system
(2) The suggested approach uses a solar power system and stores the electricity in a battery to reduce the workload of the power installation on the farm
Summary
The world is currently facing energy and food crises; many countries are transforming farmland into industrial land for related usage because they are transforming from being undeveloped to being emerging countries. Precision Agriculture (PA) works to detect relevant environmental information around the farmland to enhance automated production; the PA system controls automated machinery and the related irrigation equipment while farmers will only need to calibrate the equipment and confirm correctness; this will reduce agricultural labor and ensure a good crop-growing environment Narvaez et al (2017). Due to the limited electricity offered by the solar power system, the approach detects the equipment and conducts watering tasks by LSTM to avoid energy waste, the detectors used in the experiment monitor the soil temperature and humidity, pH value, and sunlight conditions in the farmland. The robot will forecast the best time to activate the watering equipment, and when the prediction result exceeds the set suitable conditions, the server will notify the farmer This approach is an application designed for outdoor usage and is shown to be practical on a farm. The experimental result proves that the proposed method is feasible; the equipment presented is cost-efficient, which makes it wellsuited to widespread distribution and massive adoption for general applications
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