<div>Digital Forestry uses digital technology and multidisciplinary expertise to measure, monitor, and manage urban and rural forests to maximize social, economic, and ecological benefits. </div><div><br></div><div>In chapter 2, we investigated the potential use of CNNs for hardwood lumber identification based on tangential plane images. In chapter 3, we developed deep bark, a lightweight tree species identification application, by using deep learning. In chapter 4, we first introduced a new dataset of images of hardwood species annotated for tree ring detection. We applied the state-of-art semantic segmentation models to the dataset. In chapter 5, we combined the observed classes and non-observed classes by distinguishing the attributes of objects and applied zero-shot learning to microscopic wood images. <br></div><div> </div><div>The results above chapters demonstrated the potential and effectiveness of machine learning in many forestry-related tasks. Those applications help both the research community and industry to conduct better digital forestry business. However, we still need to point out that the availability, quality, and quantity of data and annotation are critical factors in conducting meaningful research and applications in forestry.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17130083 |
| Date | 19 December 2021 |
| Creators | Fanyou Wu (11620621) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Selected_Topics_of_Deep_Learning_Application_in_Forest_Research/17130083 |
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