Automated quantification of plant water transport network failure using deep learning
| dc.contributor.advisor | Britz, Stefan | |
| dc.contributor.advisor | Moncrieff, Glenn | |
| dc.contributor.author | Naidoo, Tristan | |
| dc.date.accessioned | 2022-03-10T10:11:45Z | |
| dc.date.available | 2022-03-10T10:11:45Z | |
| dc.date.issued | 2021 | |
| dc.date.updated | 2022-03-08T10:38:16Z | |
| dc.description.abstract | Droughts, exacerbated by anthropogenic climate change, threaten plants through hydraulic failure. This hydraulic failure is caused by the formation of embolisms which block water flow in a plant's xylem conduits. By tracking these failures over time, vulnerability curves (VCs) can be created. The creation of these curves is laborious and time consuming. This study seeks to automate the creation of these curves. In particular, it seeks to automate the optical vulnerability (OV) method of determining hydraulic failure. To do this, embolisms need to be segmented across a sequence of images. Three fully convolutional models were considered for this task, namely U-Net, U-Net (ResNet34), and W-Net. The sample consisted of four unique leaves, each with its own sequence of images. Using these leaves, three experiments were conducted. They considered whether a leaf could generalise across samples from the same leaf, across different leaves of the same species, and across different species. The results were assessed on two levels; the first considered the results of the segmentation, and the second considered how well VCs could be constructed. Across the three experiments, the highest test precision-recall AUCs achieved were 81%, 45%, and 40%. W-Net performed the worst across the models, while U-Net and U-Net (ResNet-34) performed similarly to one another. VC reconstruction was assessed using two metrics. The first is Normalised Root Mean Square Error. The second is the difference in Ψ50 values between the true VC and the predicted VC, where Ψ50 is a physiological value of interest. This study found that the shape of the VCs could be reconstructed well if the model was able to recall a portion of embolisms across all images which had embolisms. Moreover, it found that some images may be more important than others due to a non-linear mapping between time and water potential. VC reconstruction was satisfactory, except for the third experiment. This study demonstrates that, in certain scenarios, automation of the OV method is attainable. To support the ubiquitous use and development of the work done in this study, a website was created to document the code base. In addition, this website contains instructions on how to interact with the code base. For more information please visit: https://plant-network-segmentation.readthedocs.io/. | |
| dc.identifier.apacitation | Naidoo, T. (2021). <i>Automated quantification of plant water transport network failure using deep learning</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/36029 | en_ZA |
| dc.identifier.chicagocitation | Naidoo, Tristan. <i>"Automated quantification of plant water transport network failure using deep learning."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2021. http://hdl.handle.net/11427/36029 | en_ZA |
| dc.identifier.citation | Naidoo, T. 2021. Automated quantification of plant water transport network failure using deep learning. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/36029 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Naidoo, Tristan AB - Droughts, exacerbated by anthropogenic climate change, threaten plants through hydraulic failure. This hydraulic failure is caused by the formation of embolisms which block water flow in a plant's xylem conduits. By tracking these failures over time, vulnerability curves (VCs) can be created. The creation of these curves is laborious and time consuming. This study seeks to automate the creation of these curves. In particular, it seeks to automate the optical vulnerability (OV) method of determining hydraulic failure. To do this, embolisms need to be segmented across a sequence of images. Three fully convolutional models were considered for this task, namely U-Net, U-Net (ResNet34), and W-Net. The sample consisted of four unique leaves, each with its own sequence of images. Using these leaves, three experiments were conducted. They considered whether a leaf could generalise across samples from the same leaf, across different leaves of the same species, and across different species. The results were assessed on two levels; the first considered the results of the segmentation, and the second considered how well VCs could be constructed. Across the three experiments, the highest test precision-recall AUCs achieved were 81%, 45%, and 40%. W-Net performed the worst across the models, while U-Net and U-Net (ResNet-34) performed similarly to one another. VC reconstruction was assessed using two metrics. The first is Normalised Root Mean Square Error. The second is the difference in Ψ50 values between the true VC and the predicted VC, where Ψ50 is a physiological value of interest. This study found that the shape of the VCs could be reconstructed well if the model was able to recall a portion of embolisms across all images which had embolisms. Moreover, it found that some images may be more important than others due to a non-linear mapping between time and water potential. VC reconstruction was satisfactory, except for the third experiment. This study demonstrates that, in certain scenarios, automation of the OV method is attainable. To support the ubiquitous use and development of the work done in this study, a website was created to document the code base. In addition, this website contains instructions on how to interact with the code base. For more information please visit: https://plant-network-segmentation.readthedocs.io/. DA - 2021_ DB - OpenUCT DP - University of Cape Town KW - Advanced Analytics LK - https://open.uct.ac.za PY - 2021 T1 - Automated quantification of plant water transport network failure using deep learning TI - Automated quantification of plant water transport network failure using deep learning UR - http://hdl.handle.net/11427/36029 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/36029 | |
| dc.identifier.vancouvercitation | Naidoo T. Automated quantification of plant water transport network failure using deep learning. []. ,Faculty of Science ,Department of Statistical Sciences, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36029 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Advanced Analytics | |
| dc.title | Automated quantification of plant water transport network failure using deep learning | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |