Prediction of crop growth environmental data using LSTM
Traditional greenhouse crop growth monitoring system has the disadvantages of poor control flexibility and low accuracy. To address these issues, this paper focuses on developing a closed-loop crop growth monitoring system for purpose of smart agriculture and employing the univariate long short-term...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press (China Science Publishing & Media Ltd.)
2024-09-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
| Subjects: | |
| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2669 |
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| Summary: | Traditional greenhouse crop growth monitoring system has the disadvantages of poor control flexibility and low accuracy. To address these issues, this paper focuses on developing a closed-loop crop growth monitoring system for purpose of smart agriculture and employing the univariate long short-term memory (LSTM) prediction model for the crop growth environmental data prediction. Based on optimizing the time-step parameter, the prediction accuracy of the univariate LSTM prediction model conducted with different prediction steps was discussed. Then the model stability was verified by using the testing datasets of different time periods. Finally, the prediction results obtained from the proposed method were compared with those obtained from least alosolute shrinkage and selection operator (LASSO), random forest regression, bidirectional LSTM, and encoder-decoder LSTM. The experimental results show that the univariate LSTM has better prediction accuracy and stability in comparison with other prediction models. The designed closed-loop crop growth monitoring system can effectively predict the environmental data, which can provide effective data support for the intelligent control of crop monitoring system. |
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| ISSN: | 1000-2618 |