Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping
Despite being an important economic component of Taif region and the Kingdom of Saudi Arabia (KSA) as a whole, Taif rose experiences challenges because of uncontrolled conditions. In this study, we developed a phenotyping prediction model using deep learning (DL) that used simple and accurate method...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/4/807 |
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| author | Hala M. Abdelmigid Mohammed Baz Mohammed A. AlZain Jehad F. Al-Amri Hatim Ghazi Zaini Matokah Abualnaja Maissa M. Morsi Afnan Alhumaidi |
| author_facet | Hala M. Abdelmigid Mohammed Baz Mohammed A. AlZain Jehad F. Al-Amri Hatim Ghazi Zaini Matokah Abualnaja Maissa M. Morsi Afnan Alhumaidi |
| author_sort | Hala M. Abdelmigid |
| collection | DOAJ |
| description | Despite being an important economic component of Taif region and the Kingdom of Saudi Arabia (KSA) as a whole, Taif rose experiences challenges because of uncontrolled conditions. In this study, we developed a phenotyping prediction model using deep learning (DL) that used simple and accurate methods to obtain and analyze data collected from ten rose farms. To maintain broad applicability and minimize computational complexity, our model utilizes a complementary learning approach in which both spatial and temporal instances of each dataset are processed simultaneously using three state-of-the-art deep neural networks: (1) convolutional neural network (CNN) to treat the image, (2) long short-term memory (LSTM) to treat the timeseries and (3) fully connected multilayer perceptions (MLPs)to obtain the phenotypes. As a result, this approach not only consolidates the knowledge gained from processing the same data from different perspectives, but it also leverages on the predictability of the model under incomplete or noisy datasets. An extensive evaluation of the validity of the proposed model has been conducted by comparing its outcomes with comprehensive phenotyping measurements taken from real farms. This evaluation demonstrates the ability of the proposed model to achieve zero mean absolute percentage error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MAPE</mi></mrow></semantics></math></inline-formula>) and mean square percentage error (MSPE) within a small number of epochs and under different training to testing schemes. |
| first_indexed | 2024-03-09T11:18:20Z |
| format | Article |
| id | doaj.art-83dc21091d154d2e8837f7465c4382b0 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T11:18:20Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-83dc21091d154d2e8837f7465c4382b02023-12-01T00:26:15ZengMDPI AGAgronomy2073-43952022-03-0112480710.3390/agronomy12040807Spatiotemporal Deep Learning Model for Prediction of Taif Rose PhenotypingHala M. Abdelmigid0Mohammed Baz1Mohammed A. AlZain2Jehad F. Al-Amri3Hatim Ghazi Zaini4Matokah Abualnaja5Maissa M. Morsi6Afnan Alhumaidi7Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Chemistry, Faculty of Applied Science, Umm Al-Qura University, Makkah 24230, Saudi ArabiaDepartment of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDespite being an important economic component of Taif region and the Kingdom of Saudi Arabia (KSA) as a whole, Taif rose experiences challenges because of uncontrolled conditions. In this study, we developed a phenotyping prediction model using deep learning (DL) that used simple and accurate methods to obtain and analyze data collected from ten rose farms. To maintain broad applicability and minimize computational complexity, our model utilizes a complementary learning approach in which both spatial and temporal instances of each dataset are processed simultaneously using three state-of-the-art deep neural networks: (1) convolutional neural network (CNN) to treat the image, (2) long short-term memory (LSTM) to treat the timeseries and (3) fully connected multilayer perceptions (MLPs)to obtain the phenotypes. As a result, this approach not only consolidates the knowledge gained from processing the same data from different perspectives, but it also leverages on the predictability of the model under incomplete or noisy datasets. An extensive evaluation of the validity of the proposed model has been conducted by comparing its outcomes with comprehensive phenotyping measurements taken from real farms. This evaluation demonstrates the ability of the proposed model to achieve zero mean absolute percentage error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MAPE</mi></mrow></semantics></math></inline-formula>) and mean square percentage error (MSPE) within a small number of epochs and under different training to testing schemes.https://www.mdpi.com/2073-4395/12/4/807Taif rosemachine learningphenotypic traitsbreedingsustainable agriculture |
| spellingShingle | Hala M. Abdelmigid Mohammed Baz Mohammed A. AlZain Jehad F. Al-Amri Hatim Ghazi Zaini Matokah Abualnaja Maissa M. Morsi Afnan Alhumaidi Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping Agronomy Taif rose machine learning phenotypic traits breeding sustainable agriculture |
| title | Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping |
| title_full | Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping |
| title_fullStr | Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping |
| title_full_unstemmed | Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping |
| title_short | Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping |
| title_sort | spatiotemporal deep learning model for prediction of taif rose phenotyping |
| topic | Taif rose machine learning phenotypic traits breeding sustainable agriculture |
| url | https://www.mdpi.com/2073-4395/12/4/807 |
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