Loading...
Please wait, while we are loading the content...
Similar Documents
Leaf Segmentation and Classification with a Complicated Background Using Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Yang, Kunlong Zhong, Weizhen Li, Fengguo |
| Copyright Year | 2020 |
| Description | The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied. First, more than 2500 leaf images with a complicated background are collected and artificially labeled with target pixels and background pixels. Two-thousand of them are fed into a Mask Region-based Convolutional Neural Network (Mask R-CNN) to train a model for leaf segmentation. Then, a training set that contains more than 1500 training images of 15 species is fed into a very deep convolutional network with 16 layers (VGG16) to train a model for leaf classification. The best hyperparameters for these methods are found by comparing a variety of parameter combinations. The results show that the average Misclassification Error ( |
| Starting Page | 1721 |
| e-ISSN | 20734395 |
| DOI | 10.3390/agronomy10111721 |
| Journal | Agronomy |
| Issue Number | 11 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-06 |
| Access Restriction | Open |
| Subject Keyword | Agronomy Remote Sensing Deep Learning Image Segmentation Mask R-cnn Plant Classification Vgg16 |
| Content Type | Text |
| Resource Type | Article |
