Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model
We developed an internal environment prediction model for smart greenhouses using machine learning models. Machine learning models were developed by finding certain rules based on the data obtained from the target system and have the advantage of learning various characteristics that are difficult t...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/11/2545 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217006210482176 |
|---|---|
| author | Kwang Cheol Oh Sunyong Park Seok Jun Kim La Hoon Cho Chung Geon Lee Dae Hyun Kim |
| author_facet | Kwang Cheol Oh Sunyong Park Seok Jun Kim La Hoon Cho Chung Geon Lee Dae Hyun Kim |
| author_sort | Kwang Cheol Oh |
| collection | DOAJ |
| description | We developed an internal environment prediction model for smart greenhouses using machine learning models. Machine learning models were developed by finding certain rules based on the data obtained from the target system and have the advantage of learning various characteristics that are difficult to define theoretically. However, the model accuracy and precision can change according to the model structure (hyperparameters, algorithms, epoch) and data characteristics. In this study, the analysis was performed according to the collected weather data characteristics. The model performance was low when the amount of training data was obtained over less than three days (4320 ea). The model performance improved with an increase in the amount of training data. Model performance stabilized when the training data volume exceeded seven days (10,080 ea). The optimal amount of data was determined to be between three and seven days, with an average model r<sup>2</sup> of 0.8811 and an RMSE of 2.056 for the gated recurrent unit algorithm. This study verified the possibility of developing a predictive model for the internal environment of a greenhouse based on weather data from outside. This study is limited to a specific target greenhouse, and further analysis of data from various greenhouses and climates is necessary to achieve global optimization. |
| format | Article |
| id | doaj-art-8a9ac40ed47b4ceaa12b02dab62fdff0 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-8a9ac40ed47b4ceaa12b02dab62fdff02025-08-20T02:08:11ZengMDPI AGAgronomy2073-43952024-10-011411254510.3390/agronomy14112545Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction ModelKwang Cheol Oh0Sunyong Park1Seok Jun Kim2La Hoon Cho3Chung Geon Lee4Dae Hyun Kim5Agriculture and Life Science Research Institute, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaAgriculture and Life Science Research Institute, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaNational Academy of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of KoreaAgriculture and Life Science Research Institute, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaWe developed an internal environment prediction model for smart greenhouses using machine learning models. Machine learning models were developed by finding certain rules based on the data obtained from the target system and have the advantage of learning various characteristics that are difficult to define theoretically. However, the model accuracy and precision can change according to the model structure (hyperparameters, algorithms, epoch) and data characteristics. In this study, the analysis was performed according to the collected weather data characteristics. The model performance was low when the amount of training data was obtained over less than three days (4320 ea). The model performance improved with an increase in the amount of training data. Model performance stabilized when the training data volume exceeded seven days (10,080 ea). The optimal amount of data was determined to be between three and seven days, with an average model r<sup>2</sup> of 0.8811 and an RMSE of 2.056 for the gated recurrent unit algorithm. This study verified the possibility of developing a predictive model for the internal environment of a greenhouse based on weather data from outside. This study is limited to a specific target greenhouse, and further analysis of data from various greenhouses and climates is necessary to achieve global optimization.https://www.mdpi.com/2073-4395/14/11/2545smart greenhouseartificial intelligence modelmachine learningdata characteristics |
| spellingShingle | Kwang Cheol Oh Sunyong Park Seok Jun Kim La Hoon Cho Chung Geon Lee Dae Hyun Kim Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model Agronomy smart greenhouse artificial intelligence model machine learning data characteristics |
| title | Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model |
| title_full | Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model |
| title_fullStr | Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model |
| title_full_unstemmed | Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model |
| title_short | Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model |
| title_sort | data driven optimization method for recurrent neural network algorithm greenhouse internal temperature prediction model |
| topic | smart greenhouse artificial intelligence model machine learning data characteristics |
| url | https://www.mdpi.com/2073-4395/14/11/2545 |
| work_keys_str_mv | AT kwangcheoloh datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel AT sunyongpark datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel AT seokjunkim datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel AT lahooncho datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel AT chunggeonlee datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel AT daehyunkim datadrivenoptimizationmethodforrecurrentneuralnetworkalgorithmgreenhouseinternaltemperaturepredictionmodel |