Automatic Plant Detection Using HOG and LBP Features With SVM

  • Mohammad Aminul Islam Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
  • Md. Sayeed Iftekhar Yousuf Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
  • M. M. Billah Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
Keywords: Plant Detection, HOG, LBP, SVM.

Abstract

Plants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun.  They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques. 

References

Christenhusz, Maarten JM, and James W. Byng. “The number of known plants species in the world and its annual increase”. Phytotaxa 261.3 (2016): 201-217.

J. X. Du, X. F. Wang, and G. J. Zhang. “Leaf shape based plant species recognition”. Applied mathematics and computation, 185(2):883–893, 2007.

A. Hong, G. Chen, J. Li, Z. Chi, and D. Zhang. “A flower image retrieval method based on roi feature”. Journal of Zhejiang University-Science A, 5(7):764– 772, 2004.

Z. Miao, M.-H. Gandelin, and B. Yuan. “An oopr-based rose variety recognition system”. Engineering Applications of Artificial Intelligence, vol. 19, 2006.

X.-F. Wang, J.-X. Du, and G.-J. Zhang. “Recognition of leaf images based on shape features using a hypersphere classifier”. in Proceedings of International Conference on Intelligent Computing 2005, ser. LNCS 3644. Springer, 2005.

J.-X. Du, X.-F. Wang, and G.-J. Zhang. “Leaf shape based plant species recognition”. Applied Mathematics and Computation, vol. 185, 2007.

J.-X. Du, D.-S. Huang, X.-F. Wang, and X. Gu. “Computer-aided plant species identification (capsi) based on leaf shape matching technique”. Transactions of the Institute of Measurement and Control, vol. 28, 2006.

C. Im, H. Nishida, T.L. Kunii. “Recognizing plant species by leaf shapes – a case study of the Acer family”, Proc. Pattern Recog. 2 (1998) 1171–1173.

Wang Z, Chi Z, Feng D, Wang Q. “Leaf Image Retrieval with Shape Features”. Lecture Notes in Computer Science 1929. In: Laurini R ed. Advances in Visual Information Systems. Berlin: Spring-Verlag, 477~487.

Zhang B, Zhang H: “Content Based Image Retrieval of Standard Tobacco Leaf Database”. Computer Engineering and Application.2002.07:203~205.

Q. Wu, C. Zhou, & C. Wang. “Feature Extraction and Automatic Recognition of Plant Leaf Using Artificial Neural Network”. Avances en Ciencias de la Computacion, pp. 5-12, 2006.

S. G. Wu, F. S. Bao, E. Y Xu, Y-X. Wang, Y-F. Chang, & Q-L. Xiang. “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network”. IEEE 7th Interantional Symposium on Signal Processing and Information Technology, Cairo, 2007.

A. Aakif, M.F. Khan. “Automatic classification of plants based on their leaves”, Biosyst. Eng. 139 (2015) 66–75.

J.S. Cope, P. Remagnino. “Classifying plant leaves from their margins using dynamic time warping”, in: International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, 2012, pp. 258–267.

A.R. Backes, O.M. Bruno. “Plant leaf identification using multi-scale fractal dimension”, in: International Conference on Image Analysis and Processing, Springer, 2009, pp. 143–150.

J.S. Cope, P. Remagnino , S. Barman , P. Wilkin . “Plant texture classification using Gabor co-occurrences”. in: International Symposium on Visual Computing, Springer, 2010, pp. 669–677 .

M. Rashad , B. El-Desouky , M.S. Khawasik . “Plants images classification based on textural features using combined classifier”. Int. J. Comput. Sci. Inf. Technol. 3 (4) (2011) 93–100.

A. Olsen, S. Han, B. Calvert, P. Ridd, O. Kenny. “In situ leaf classification using histograms of oriented gradients”, in: International Conference on Digital Image Computing, 2015, pp. 1–8 .

J. Charters, Z. Wang, Z. Chi, A.C. Tsoi, D.D. Feng. “EAGLE: a novel descriptor for identifying plant species using leaf lamina vascular features”. in: ICME-Work- shop, 2014, pp. 1–6.

M.G. Larese, R. Namías, R.M. Craviotto, M.R. Arango, C. Gallo, P.M. Granitto. “Automatic classification of legumes using leaf vein image features”, Pattern Recognit. 47 (1) (2014) 158–168.

G.L. Grinblat, L.C. Uzal, M.G. Larese, P.M. Granitto. “Deep learning for plant identification using vein morphological patterns”. Comput. Electron. Agric. 127 (2016) 418–424.

J. Chaki, R. Parekh, S. Bhattacharya. “Plant leaf recognition using texture and shape features with neural classifiers”, Pattern Recognit. Lett. 58 (2015) 61–68.

S. Mouine, I. Yahiaoui, A. Verroust-Blondet. “Advanced shape context for plant species identification using leaf image retrieval”. in: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, 2012, p. 49 .

T. Beghin , J.S. Cope , P. Remagnino , S. Barman. “Shape and texture based plant leaf classification”. in: International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, 2010, pp. 345–353 .

Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). “Novel algorithm for remote estimation of vegetation fraction”. Remote Sensing of Environment, 80, 76e87, Elsevier.

Dalal, N. and B. Triggs. “Histograms of Oriented Gradients for Human Detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1 (June 2005), pp. 886–893.

Carcagnì, Pierluigi, et al. “Facial expression recognition and histograms of oriented gradients: a comprehensive study.” SpringerPlus 4.1 (2015): 645.

T. Ojala, M. Pietikäinen, D. Harwood. “A comparative study of texture measures with classification based on featured distribution”. Pattern Recognition 29 (1) (1996) 51–59.

Sarraf, Saman. “Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours”. International Journal of Computer (IJC). 24. 56-79.

Camargo, A., and J. S. Smith. “Image pattern classification for the identification of disease causing agents in plants.” Computers and Electronics in Agriculture 66.2 (2009): 121-125.

Hsu, C.W., Lin, C.J., 2002. “A comparison of methods for Multi-class Support Vector Machines.” IEEE Transaction on neural networks 13 (2), 415–425.

Angulo, C., Parra, X., Catala, A., 2003. K-SVRC. “A Support Vector Machine for multiclass classification”. Neurocomputing 55, 57–77.

J.C. Neto , G.E. Meyer , D.D. Jones , A.K. Samal , Plant species identification us- ing elliptic fourier leaf shape analysis, Comput. Electron. Agric. 50 (2) (2006) 121–134 .

Published
2019-04-28
How to Cite
Aminul Islam, M., Iftekhar Yousuf, M. S., & M. Billah, M. (2019). Automatic Plant Detection Using HOG and LBP Features With SVM. International Journal of Computer (IJC), 33(1), 26-38. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1384
Section
Articles