Abstract
Plant identification is a critical step in protecting plant diversity. However, many existing identification systems prohibitively rely on hand-crafted features for plant species identification. In this paper, a deep learning method is employed to extract discriminative features from plant images along with a linear SVM for plant identification. To offer a self-learning feature representation for different plant organs, we choose a very deep convolutional neural networks (CNNs), which consists of sixteen convolutional layers followed by three Fully-Connected (FC) layers and a final soft-max layer. Five max-pooling layers are performed over a 2×2 pixel window with stride 2. Extensive experiments on several plant datasets demonstrate the remarkable performance of the very deep neural network compared to the hand-crafted features.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Nguyen QK, Le TL, Pham NH (2013) Leaf based plant identification system for android using surf features in combination with bag of words model and supervised learning. In: Proceedings of international conference on advanced technologies for communications, pp 404–407
Ahmed N, Ghani U, Asif S (2016) An automatic leaf based plant identification system. In: Proceedings of the 5th international multidisciplinary conference, pp 29–31
Arora A, Gupta A, Bagmar N, Mishra S, Bhattacharya A (2012) A plant identification system using shape and morphological features on segmented leaflets. CLEF
Barré P, Stöver BC, Müller KF, Steinhage V (2017) A computer vision system for automatic plant species identification. Ecological Informatics 40:50–56
Belhumeur PN, Chen D, Feiner S, Jacobs D, Kress W, Ling H, Lopez I, Ramamoorthi R, Sheorey S, White S, Zhang L (2008) Searching the world’s herbaria: A system for visual identification of plant species. In: Proceedings of 10th european conference on computer vision, vol 4, pp 116–129
Bo L, Ren X, Fox D (2013) Multipath sparse coding using hierarchical matching pursuit. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 660–667
Boureau Y-L, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2559–2566
Caballero C, Aranda MC (2010) Plant species identification using leaf image retrieval. In: Proceedings of the ACM international conference on image and video retrieval, pp 327–334
Chaki J, Parekh R (2011) Plant leaf recognition using shape based features and neural network classifiers. Int J Adv Comput Sci Appl 2(10):41–47
Chopra M (2015) Treeid: An image recognition system for plant species identification. Report in cs231n of Stanford University
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: A deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31st international conference on machine learning, vol 32, pp 647–655
Du J-X, Wang X-F, Zhang G-J (2007) Leaf shape based plant species recognition. Appl Math Comput 185(2):883–893
Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: Proceedings of IEEE international conference on computer vision, pp 2018–2025
Fiel S, Sablatnig R (2011) Automated identification of tree species from images of the bark, leaves and needles. In: Proceedings of 16th computer vision winter workshop
Goau H, Bonnet P, Barbe J, Bakic V, Joly A, Molino J-F, Barthelemy D, Boujemaa N (2012) Multi-organ plant identification. In: Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data, pp 41–44
Goau H, Bonnet P, Joly A (2015) Lifeclef plant identification task 2015. CLEF
Goau H, Bonnet P, Joly A (2016) Plant identi cation in an open-world (lifeclef 2016). In: Proceedings of conference and labs of the evaluation forum, pp 428–439
Guo Y, Ding G, Han J, Gao Y (2017) Zero-shot learning with transferred samples. IEEE Trans Image Process 26(7):3277–3290
Guo Y, Ding G, Li L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26 (3):1344–1354
Guru DS, Sharath Kumar YH, Shantharamu M (2010) Texture features and knn in classification of flower images. Recent Trends in Image Processing and Pattern Recognition 37(1):21–29
Hsiao J-K, Kang L-W, Chang C-L, Hsu C-Y, Chen C-Y (2014) Learning sparse representation for leaf image recognition. In: Proceedings of IEEE conference on consumer electronics, pp 209–210
Hsiao J-K, Kang L-W, Chang CL, Lin CY (2014) Comparative study of leaf image recognition with a novel learning-based approach. In: Proceedings of science and information conference, pp 389–393
Jiang F, Zhang S, Wu S, Gao Y, Zhao D (2015) Multi-layered gesture recognition with kinect. J Mach Learn Res 16:227–254
Kim S-J, Kim B-W, Kim D-P (2011) Tree recognition for landscape using by combination of features of its leaf, flower and bark. In: Proceedings of SICE annual conference, pp 1147–1151
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Kulkarni T, Uke NJ (2014) Implementation of image based flower classification system. International Journal of Computer Science and Business Informatics 13(1):35–44
Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez IC, Soares JVB (2012) Leafsnap: A computer vision system for automatic plant species identification. In: Proceedings of European Conference on Computer Vision, pp 502–516
Kumar TP, Veera Prasad Reddy M, Bora PK (2016) Leaf identification using shape and texture features. In: Proceedings of international conference on computer vision and image processing, vol 460, pp 531–541
Kumar TP, Veera Prasad Reddy M, Bora PK (2017) Leaf identification using shape and texture features. In: Proceedings of international conference on computer vision and image processing, pp 531–541
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, vol 2, pp 2169–2178
Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, pp 609–616
Lee K-B, Hong K-S (2013) An implementation of leaf recognition system using leaf vein and shape. International Journal of Bio-Science and Bio-Technology 5(2):57–66
Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-plant: Plant identification with convolutional neural networks. In: Proceedings of IEEE international conference on image processing, pp 452–456
Lin Z, Ding G, Han J, Wang J (2017) Cross-view retrieval via probability-based semantics-preserving hashing. IEEE Transactions on Cybernetics 47(12):4342–4355
Liu Q, Lu X, He Z, Zhang C, Chen W-S (2017) Deep convolutional neural networks for thermal infrared object tracking. Knowl-Based Syst 134:189–198
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Ma L-H, Zhao Z-Q, Wang J (2013) Apleafis: an android-based plant leaf identification system. In: Proceedings of intelligent computing theories, pp 106–111
Mabrouk AB, Najjar A, Zagrouba E (2014) Image flower recognition based on a new method for color feature extraction. In: Proceedings of the 9th international conference on computer vision theory and applications, vol 2, pp 201–206
Metre V, Ghorpade J (2013) An overview of the research on texture based plant leaf classification. International Journal of Computer Science and Network 2(3):25–36
Mouine S, Yahiaoui I, Verroust-Blondet A (2012) Advanced shape context for plant species identification using leaf image retrieval. In: Proceedings of the 2nd ACM international conference on multimedia retrieval
Mouine S, Yahiaoui I, Verroust-Blondet A, Joyeux L, Selmi S, Goau H (2013) An android application for leaf-based plant identification. In: Proceedings of the 3rd ACM conference on International conference on multimedia retrieval, pp 309–310
Nilsback M-E, Zisserman A (2008) Automated flower classification over a large number of classes. In: Proceedings of indian conference on computer vision, graphics and image processing, pp 722–729
Pallavi P, Veena Devi VS (2014) Leaf recognition based on feature extraction and zernike moments. In: Proceedings of international conference on advances in computer & communication engineering, vol 2, pp 67–73
Patel HN, Jain RK, Joshi MV (2011) Fruit detection using improved multiple features based algorithm. Int J Comput Appl 13(2):1–5
Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang M-H Hedging deep features for visual tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence. In Press
Ren X-M, Wang X-F, Zhao Y (2012) An efficient multi-scale overlapped block lbp approach for leaf image recognition. In: Proceedings of the 8th international conference on intelligent computing theories and applications, pp 237–243
Satti V, Satya A, Sharma S (2013) An automatic leaf recognition system for plant identification using machine vision technology. International Journal of Engineering Science and Technology 5(4):874– 879
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: Proceedings of computer vision and pattern recognition
Singh K, Gupta I, Gupta S (2010) Svm-bdt pnn and fourier moment technique for of leaf shape. International journal of signal processing, Image Processing and Pattern Recognition 3(4):67–78
Sünderhauf N, McCool C, Upcroft B, Perez T (2014) Fine-grained plant classification using convolutional neural networks for feature extraction. CLEF
Sun B-Y, Huang D-S, Guo L, Zhao Z-Q (2004) Support vector machine committee for classification. In: Proceedings of international symposium on neural networks, pp 648–653
Tsolakidis DG, Kosmopoulos DI, Papadourakis G (2014) Plant leaf recognition using zernike moments and histogram of oriented gradients. In: Proceedings of artificial intelligence: methods and applications, pp 406–417
Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 3360–3367
Wang X, Huang D-S, Du J-X, Xu H, Heutte L (2008) Classification of plant leaf images with complicated background. Appl Math Comput 205(2):916–926
Wang Z, Chi Z, Feng D (2003) Shape based leaf image retrieval. IEE Proceedings on Vision, Image and Signal Processing 150(1):34–43
Wang Z, Lu B, Chi Z, Feng D (2011) Leaf image classification with shape context and sift descriptors. In: Proceedings of international conference on digital image computing: techniques and applications, pp 650–654
Wilf P, Zhang S, Chikkerur S, Little SA, Wing SL, Serre T (2016) Computer vision cracks the leaf code. Proc Natl Acad Sci USA 113(12):3305–3310
Xiao X-Y, Hu R, Zhang S-W, Wang X-F (2010) Hog-based approach for leaf classification. In: Proceedings of lecture notes in computer science, vol 6216, pp 149–155
Song Y, Glasbey CA, Horgan GW, Polder G, Dieleman JA, Van der Heijden GWAM (2014) Automatic fruit recognition and counting from multiple images. Biosyst Eng 118(1):203–215
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1794–1801
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of european conference on computer vision, pp 818–833
Zhang B, Yang Y, Chen C, Yang L, Han J, Shao L (2017) Action recognition using 3d histograms of texture and a multi-class boosting classifier. IEEE Trans Image Process 26(10):4648–4660
Zhang C, Zhou P, Li C, Liu L (2015) A convolutional neural network for leaves recognition using data augmentation. In: Proceedings of IEEE international conference on computer and information technology, pp 2143–2150
Zhang S, Lan X, Qi Y, Yuen PC (2017) Robust visual tracking via basis matching. IEEE Trans Circuits and Systems for Video Technology 27(3):421–430
Zhang S, Lan X, Yao H, Zhou H, Tao D, Li X (2017) A biologically inspired appearance model for robust visual tracking. IEEE Trans Neural Networks and Learning Systems 28(10):2357–2370
Zhang S, Qi Y, Jiang F, Lan X, Yuen PC, Zhou H Point-to-set distance metric learning on deep representations for visual tracking. IEEE Trans. on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2017.2766093
Zhang S, Zhou H, Jiang F, Li X (2015) Robust visual tracking using structurally random projection and weighted least squares. IEEE Transactions on Circuits and Systems for Video Technology 25(11):1749–1760
Zhao C, Chan SSF, Cham W-K, Chu LM (2015) Plant identification using leaf shapes - a pattern counting approach. Pattern Recogn 48(10):3203–3215
Zhao Z, Huang X, Yang G (2015) Plant recognition based on leaf and bark images. Journal of Computational Information Systems 11(3):857–864
Zhao Z-Q, Xie B-J, Cheung YM, Wu X (2014) Plant leaf identification via a growing convolution neural network with progressive sample learning. In: Proceedings of asian conference on computer vision, vol 2, pp 348–361
Zhu H, Huang X, Zhang S, Yuen PC (2017) Plant identification via multipath sparse coding. Multimedia Tools and Applications 76(3):4599–4615
Acknowledgments
This work is supported by the key R&D program of Yantai City (No. 2016YT06000609).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, H., Liu, Q., Qi, Y. et al. Plant identification based on very deep convolutional neural networks. Multimed Tools Appl 77, 29779–29797 (2018). https://doi.org/10.1007/s11042-017-5578-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-5578-9