Name
Title | ||
|---|---|---|
Bringing Deep Learning to the Fields and Forests: Leaf Reconstruction and Shape Estimation |
Abstract | ||
|---|---|---|
One of the indicators of ecosystem health is leaf health. Among the leading indicators studied in leaves, herbivory and disease presence are relevant indicators of ecosystem behavior. Several methods in the literature study leaf damage estimation processes. Most of the previous studies display the usage of methods that display limited generalism. In previous work, we displayed the possibility of using conditional GANs to estimate the leaf damage. Our results displayed that this approach increases the generalism of the solution by seeking to reconstruct the original leaf shape. In this paper, we present a deeper discussion on the results and a previous discussion on how to transport this method to the forests. We present the whole method used for approximating the leaf shape, assessing further results that display the method’s robustness. Finally, we also show preliminary methods that can be used to embed this method in edge computing hardware. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s42979-022-01082-4 | SN Computer Science |
Keywords | DocType | Volume |
Conditional GAN, Leaf shape estimation, Defoliation estimation | Journal | 3 |
Issue | ISSN | Citations |
3 | 2662-995X | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silva Mateus C. | 1 | 0 | 0.34 |
| Bianchi Andrea G. C. | 2 | 0 | 0.34 |
| Ribeiro Servio P. | 3 | 0 | 0.34 |
| Oliveira Ricardo A. R. | 4 | 0 | 0.34 |