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Using Long Short-Term Memory for Building Outdoor Agricultural Machinery
Chien-Hung Wu1, Chun-Yi Lu2, Jun-We Zhan3
1Department of Marine Recreation, National Penghu University of Science and Technology, Magong, Taiwan.
Frontiers in Neurorobotics
|July 17, 2020
Summary
This study introduces an agricultural robot using Long Short-Term Memory (LSTM) to optimize crop yields amidst climate change and population growth. The robot efficiently manages resources, ensuring suitable growing conditions and boosting food production.
Area of Science:
- Agricultural Engineering
- Artificial Intelligence
- Environmental Science
Background:
- Climate change and population growth are exacerbating agricultural output decline and food crises.
- Farmland transformation in emerging economies further stresses food production.
- Outdoor farms often lack essential electricity and water resources.
Purpose of the Study:
- To propose an innovative outdoor agricultural robot for enhanced crop production.
- To address resource allocation challenges in resource-scarce farming environments.
- To leverage artificial intelligence for predictive environmental control and resource management.
Main Methods:
- Development of a portable, green-powered agricultural robot.
- Integration of Long Short-Term Memory (LSTM) for environmental and weather forecast analysis.
- Utilization of multivariate LSTM for predicting variables and controlling solar power supply.
Main Results:
- The robot effectively detects environmental conditions for precise resource control (water and electricity).
- The system demonstrates potential for significant increases in agricultural output.
- LSTM-based predictions enable optimized power management from solar energy.
Conclusions:
- The developed agricultural robot offers a viable solution for improving crop yields in challenging environments.
- Intelligent resource management through AI is crucial for sustainable agriculture.
- The robot's design addresses critical needs for electricity and water in outdoor farming settings.
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