A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

By Stephen Gang Wu, F. S. Bao, E. Xu, Yuxuan Wang, Yi-Fan Chang, Qiao-Liang Xiang. 2007

983 citations recorded by the source corpus.

In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Plant Science, Representation & Self-Supervised Learning

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “While plant related datasets exist for leaf or ﬂower recognition (Go¨eau et al., 2012; Silva et al., 2013; Wu et al., 2007; Nilsback and Zisser-man, 2010), these datasets were obtained in an uncontrolled or destructive manner and not in a phenotyping context.”
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017, methodology): “These refer to basic geometric properties of the leaf’s shape, i.e., diameter, major axis length, minor axis length, area, perimeter, centroid (see, e.g., [144]).”
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning (2018, background): “This sprawling dataset is quite unique, with most other works presenting site-speciﬁc datasets for their weeds of interest 9,14,21 .”
Automated plant species identification—Trends and future directions (2018, methodology): “Flavia [24] 32 1,907 scan + photo plain leaves trees”
How deep learning extracts and learns leaf features for plant classification (2017, methodology): “A number of approaches have been proposed in the literature for automatic analysis of botanical organs, such as leaves and flowers [4, 5, 6].”
Plant species identification using digital morphometrics: A review (2012, result): “Another study using shape features was carried out by Wu et al. (2007), who used an artificial neural network to identify 32 species of Chinese plants from images of single leaves, and compared the results against a number of other classifiers.”
Multi-modality imagery database for plant phenotyping (2016, methodology): “The Flavia database [Wu et al., 2007] is considerably larger and a neural network is utilized to train a leaf classifier.”