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Agronomy
Volume 12
Issue 4
10.3390/agronomy12040807
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Article

Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping

by
Hala M. Abdelmigid
1,*,
Mohammed Baz
2,
Mohammed A. AlZain
3,
Jehad F. Al-Amri
3,
Hatim Ghazi Zaini
2,
Matokah Abualnaja
4,
Maissa M. Morsi
5 and
Afnan Alhumaidi
1
1
Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
2
Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
3
Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
4
Department of Chemistry, Faculty of Applied Science, Umm Al-Qura University, Makkah 24230, Saudi Arabia
5
Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(4), 807; https://doi.org/10.3390/agronomy12040807
Submission received: 4 February 2022 / Revised: 24 March 2022 / Accepted: 24 March 2022 / Published: 27 March 2022
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Abstract

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 (MAPE) and mean square percentage error (MSPE) within a small number of epochs and under different training to testing schemes.
Keywords: Taif rose; machine learning; phenotypic traits; breeding; sustainable agriculture Taif rose; machine learning; phenotypic traits; breeding; sustainable agriculture

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MDPI and ACS Style

Abdelmigid, H.M.; Baz, M.; AlZain, M.A.; Al-Amri, J.F.; Zaini, H.G.; Abualnaja, M.; Morsi, M.M.; Alhumaidi, A. Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping. Agronomy 2022, 12, 807. https://doi.org/10.3390/agronomy12040807

AMA Style

Abdelmigid HM, Baz M, AlZain MA, Al-Amri JF, Zaini HG, Abualnaja M, Morsi MM, Alhumaidi A. Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping. Agronomy. 2022; 12(4):807. https://doi.org/10.3390/agronomy12040807

Chicago/Turabian Style

Abdelmigid, Hala M., Mohammed Baz, Mohammed A. AlZain, Jehad F. Al-Amri, Hatim Ghazi Zaini, Matokah Abualnaja, Maissa M. Morsi, and Afnan Alhumaidi. 2022. "Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping" Agronomy 12, no. 4: 807. https://doi.org/10.3390/agronomy12040807

APA Style

Abdelmigid, H. M., Baz, M., AlZain, M. A., Al-Amri, J. F., Zaini, H. G., Abualnaja, M., Morsi, M. M., & Alhumaidi, A. (2022). Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping. Agronomy, 12(4), 807. https://doi.org/10.3390/agronomy12040807

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