| Author: | Sun, Guoqing |
| Title: | Plant recognition through deep learning |
| Advisors: | Chi, Zheru (EIE) |
| Degree: | M.Sc. |
| Year: | 2019 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Plants -- Classification Image processing Machine learning |
| Department: | Faculty of Engineering |
| Pages: | iv, 40 pages : color illustrations |
| Language: | English |
| Abstract: | Plant is an important contributor of earth ecosystem. It is not only the largest source of oxygen but also the daily food of other creatures, therefore, plant research is extremely valuable for human. To study and protect flora diversities, pant classification is a primary step. However, classification of plants is very challenging, because different types of plants sometimes present similarity and the plants vary in different time. In this dissertation, I propose to classify the plant based on a multi-organ approach. The plant organs considered includes flower, leaf, fruit, and stem. Additional data provide more information that might help to distinguish between species, but the variability in shape and appearance in plant organs raises the degree of complexity of the problem when I train the network model by multi-organ images straightly. In order to tackle this problem, I use a CNN-based method to operate on one image from a single organ, simultaneously capturing one or more organs of the same plant species. Then the three combination methods, namely the majority principle (MAV), the majority appearance principle and the majority principle (MAP&MAV), and the majority principle of sum score for different organs (S-MAV), are used to classify plants. Experimental results show that by utilizing the mentioned techniques. The proposed multi-organ method is better than single organ in this work. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022270856503411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.26 MB | Adobe PDF | View/Open |
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