Research Article
Developing an Intelligent Agricultural System Based on Long Short-Term Memory
@INPROCEEDINGS{10.1007/978-3-030-57115-3_18, author={Hsin-Te Wu and Jun-Wei Zhan and Fan-Hsun Tseng}, title={Developing an Intelligent Agricultural System Based on Long Short-Term Memory}, proceedings={Bio-inspired Information and Communication Technologies. 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings}, proceedings_a={BICT}, year={2020}, month={8}, keywords={Intelligent agricultural Long Short-Term Memory Artificial intelligence}, doi={10.1007/978-3-030-57115-3_18} }- Hsin-Te Wu
Jun-Wei Zhan
Fan-Hsun Tseng
Year: 2020
Developing an Intelligent Agricultural System Based on Long Short-Term Memory
BICT
Springer
DOI: 10.1007/978-3-030-57115-3_18
Abstract
There were many undeveloped countries upgraded to emerging countries in recent years; as a result, the farmland has been transferred to commercial or industrial lands that significantly reduce the areas of farmland, lowers down the agricultural labor force due to the population aging and further decreases agricultural output. Additionally, many of the farmland are outdoor farms, which are limited by water resources and electricity. The study develops an intelligent agricultural system based on Long Short-Term Memory (LSTM), through utilizing solar power to monitor crop environments. The key features presented in this study are 1. reducing the electrical wiring cost by using solar power; 2. adding weather forecast information to initiate the equipment and avoid the waste of electricity; 3. using the environmental monitor to check whether the crop is at a suitable environment and the system will alarm if the environment is not suitable. Through LSTM to monitor environments and lower the initiating power for avoiding electricity waste. From the experiments of the research, the method is proved to be feasible and is usable without the need for additional power-supply equipment.
