Leaf Segmentation and Classification with a Complicated Background Using Deep Learning

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...

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Published inAgronomy (Basel) Vol. 10; no. 11; p. 1721
Main Authors Yang, Kunlong, Zhong, Weizhen, Li, Fengguo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2020
Subjects
Accuracy
agronomy
Algorithms
artificial intelligence
Artificial neural networks
Cellular telephones
Classification
Deep learning
Digital cameras
Fruits
Image classification
Image processing
Image retrieval
Image segmentation
Leaves
Machine learning
Mask R-CNN
Methods
Neural networks
phenotype
Phenotypes
Pixels
plant classification
Plants
Training
VGG16
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Summary: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 (ME) of 80 test images using Mask R-CNN is 1.15%. The average accuracy value for the leaf classification of 150 test images using VGG16 is up to 91.5%. This indicates that these methods can be used to segment and classify the leaf image with a complicated background effectively. It could provide a reference for the phenotype analysis and automatic classification of plants.
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ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy10111721