Deep learning-enabled temperature simulation of a greenhouse tunnel.
| dc.contributor.author | Jogunola, O. | en_ZA |
| dc.contributor.author | Hull. K. J. | en_ZA |
| dc.contributor.author | Mabitsela, Mosima Mamoyahabo | en_ZA |
| dc.contributor.author | Phiri, E. E | en_ZA |
| dc.contributor.author | Adebisi, B. | en_ZA |
| dc.contributor.author | Booysen, M. J. | en_ZA |
| dc.date.accessioned | 2024-01-22T09:32:08Z | en_ZA |
| dc.date.available | 2024-01-22T09:32:08Z | en_ZA |
| dc.date.issued | 2023-11 | en_ZA |
| dc.description | CITATION: Booysen, MJ. et al. 2023. Deep Learning-Enabled Temperature Simulation of a Greenhouse Tunnel. Industrial Engineering. 8 pages | en_ZA |
| dc.description | The original publication is available at: http://www.advancesincleanerproduction.net | en_ZA |
| dc.description.abstract | ABSTRACT: Agriculture is poised to suffer greatly from the effects of climate change. Prediction models, using deep learning, have been developed that can simulate and predict conditions in open field farming to combat the climate variability from climate change. However, deep learning used in precision agriculture, specifically greenhouse tunnels, is under-researched despite also being affected by this variability. Utilising tunnel data collected over 42 days, two hybrid deep learning models were designed. Specifically, a hybrid of convolutional neural network (CNN) and Long Short-Term Memory (LSTM), and a hybrid of CNN and Bidirectional LSTM (BLSTM). The models are designed to forecast the internal temperature of the tunnel to support its management. The cooling wet wall state, solar irradiance, inside and outside temperature of the tunnel are input variables to the developed deep-learning models. Two scenarios are discussed with the results, the first scenario includes all the external variables as input, while the second scenario only considers the internal temperature as input. Results show a performance improvement of 48% and 14% computation time for the CNN-LSTM compared to the CNN-BLSTM model for the two scenarios, respectively. In terms of the measured loss metrics, both models had varied performance and model fitness, with an average mean square error of 0.025 across the models and scenarios. | en_ZA |
| dc.description.version | Publisher’s version | en_ZA |
| dc.format.extent | 8 pages : illustrations | en_ZA |
| dc.identifier.citation | Booysen, MJ. et al. 2023. Deep Learning-Enabled Temperature Simulation of a Greenhouse Tunnel. Industrial Engineering. 8 pages | en_ZA |
| dc.identifier.issn | 1984-8455 (online) | en_ZA |
| dc.identifier.uri | https://scholar.sun.ac.za/handle/10019.1/129193 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.publisher | IWACP | en_ZA |
| dc.rights.holder | Authors retain copyright | en_ZA |
| dc.subject.lcsh | Internet of things | en_ZA |
| dc.subject.lcsh | Deep learning (Machine learning) | en_ZA |
| dc.subject.lcsh | Precision farming | en_ZA |
| dc.subject.lcsh | Climatic changes | en_ZA |
| dc.title | Deep learning-enabled temperature simulation of a greenhouse tunnel. | en_ZA |
| dc.type | Article | en_ZA |