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Leaf Segmentation and Classification with a Complicated Background Using Deep Learning
Content Provider | MDPI |
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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 |