Leaf image recognition based on layered convolutions neural network deep learning.

ZHANG Shuai; HUAI Yong-jian.

doi:10.13332/j.1000-1522.20160035

Journal of Beijing Forestry University > 2016 > 38(9): 108-115. > DOI: 10.13332/j.1000-1522.20160035

ZHANG Shuai, HUAI Yong-jian.. Leaf image recognition based on layered convolutions neural network deep learning.[J]. Journal of Beijing Forestry University, 2016, 38(9): 108-115. DOI: 10.13332/j.1000-1522.20160035

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Leaf image recognition based on layered convolutions neural network deep learning.

School of Information, Beijing Forestry University,Beijing, 100083, P.R. China.

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Received Date: January 20, 2016
Published Date: September 29, 2016

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Abstract

Deep learning has been recently becoming research hotspot in the field of image recognition. In this study, plant leaf images were used as recognition objects. Plant leaf images were divided into single and complex background and were treated by using image segmentation method. We design a deep learning system which includes eight layers of Convolution Neural Network (CNNs) to identify leaf images. And then the deep learning system was tested with 33 293 leaf sample images which come from Pl@antNet libraries and our extending leaf libraries for image training and recognition. Compared with the traditional identification methods, the classifier provided in this paper has achieved better recognition effect. For CNN+SVM and CNN+Softmax recognition system, the simple background leaf recognition rate reaches 91.11% and 90.90%, and the recognition rate of complex background reaches 34.38%. The system has higher recognition rate for the large amount of leaf images with no more optimization, and has a higher recognition rate especially for the recognition of complex background images.
- plant recognition,
- leaf image,
- feature extraction,
- SVM,
- deep learning

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