An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop
Автор: S. B. Ullagaddi, S.Viswanadha Raju
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 9 vol.9, 2017 года.
Бесплатный доступ
Lack of apparent shape and texture features in disease recognition (Powdery Mildew and Anthracnose) of crop is a key challenge of Agriculture domain in the last few decades. The various soft computing techniques exists in computer vision system still there is need of most efficient methods to meet accuracy. In this work An enhanced Wavelet-PCA based Statistical Feature Extraction technique along with Modified Rotation Kernel Transformation (MRKT) based directional features is proposed in order to address the issues arising in different methodologies for plant disease recognition. This enhanced scheme extracts twenty wavelet features in addition to twelve direction features for different plant parts mango flower, fruit and leaf. This research work is an extended part presents in reference 1 by the authors. The feature set of total 32 features is used to train with Artificial Neural Network to diagnose both Powdery Mildew and Anthracnose disease which occur in the form of Fungus and black spots respectively on different parts of mango plant. The results obtained are found with accuracy of 98.50%, 98.75%, and 98.70% respectively for flower, fruit and leaf .
Wavelets, Principal Component Analysis (PCA), Statistical Features, Powdery Mildew, Anthracnose, Neural Network, Mean Square Error (MSE)
Короткий адрес: https://sciup.org/15014227
IDR: 15014227
Список литературы An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop
- S.B. Ullagaddi, S.Viswanadh.Raju, “Disease Recognition in Mango Crop Using Modified Rotational Kernel Transform Features” Advanced Computing and Communication Systems - ICACCS 2017.
- Y.Chai, X.Wang, “Recognition of greenhouse tomato disease based on image processing technology” Pattern Recognition and Simulation, 2013, vol.9, pp. 83-89.
- B.Zhang, J.Zhu, Y.Liu, “Image segmentation of crop leaves lesion based on the fuzzy C-means clustering,” Intelligent Computer and Applications, 2011, vol. 1, no.3, pp. 50-51.
- Tai AP, Martin MV, Heald CL (2014) “Threat to future global food security from climate change and ozone air pollution.” Nature Climate Change 4(9):817–821.
- Strange RN, Scott PR (2005) “Plant disease: a threat to global food security.” Phytopathology43.
- Neeraj Kumar, Peter N Belhumeur, Arijit Biswas, David W Jacobs, W John Kress, Ida C Lopez, andJoão VB Soares, “Leaf snap: A computer vision system for automatic plant species identification,” in ECCV, pp.502–516. Springer, 2012.
- CemKalyoncu and OnsenToygar, “Geometric leaf classification,” Computer Vision and Image Understanding” in Press, http://dx.doi.org/10.1016/j.cviu.2014.11.001.
- Abdul Kadir, Lukito Edi Nugroho, AdhiSusanto,and Paulus InsapSantosa, “Leaf classification using shape, color, and texture features,” arXiv preprintarXiv:1401.4447, 2013.
- Thibaut Beghin, James S Cope, Paolo Remagnino, and Sarah Barman, “Shape and texture based plant leaf classification,” in Advanced Concepts for Intelligent Vision Systems, 2010, pp. 345–353.
- Shiv Ram Dubey, Anand Singh Jalal (2012) “Adapted Approach for Fruit disease Identification using Images”, in International Journal of computer vision and image processing (IJCVIP) Vol. 2, no. 3:44-58.
- B. Yanikoglu, E. Aptoula, C. Tirkaz, “Automatic plant identification from photographs”, Machine Vision and Applications: 1369–1383.Springer-2014.
- Sue Han Lee, Chee Seng Chan, Paul Wilkin, Paolo Remagnino, “Deep-plant: plant identification with Convolutional neural networks”, rearXiv: 1506.08425v1, 2015
- Hughes DP, Salathé M (2015), “An open access repository of images on plant health to enablethe development of mobile disease diagnostics.” CoRRabs/1511.08060.
- Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010), “The pascal visual object classes (voc) challenge.” International journal of computer vision 88(2):303–33
- Russakovsky O et al. (2015) “Image Net Large Scale Visual Recognition Challenge.” International Journal of Computer Vision (IJCV) 115(3):211–252.
- Deng J et al. (2009) “Imagenet: A large-scale hierarchical image database in Computer Vision and Pattern Recognition” 2009. CVPR 2009. IEEE Conference on. (IEEE), pp. 248–255.
- Yinmao Song, ZhihuaDiao, Yunpeng Wang, Huan Wang, “Image Feature Extraction of Crop Disease” in IEEE Symposium on Electrical & Electronics Engineering (EEESYM), 2012
- Z.Peng, X.Si, X.Wang, H.Yuan, “Feature extraction of cucumber diseases based on computer image processing technology” Journal of Agricultural Mechanization Research, 2014, vol.02, pp.179-182,187.
- T.Liu, X.Zhong, C.Sun, W.Guo, Y.Chen, J.Sun, “Recognition of rice leaf diseases based on computer vision” Scientia AgriculturaSinica, 2014, vol.47, no.4, pp.664-674.
- G.E.Meyer, D.A.Davison, “An electronic image plant growth measurement system,” Transactions of the ASAE, 1987, vol.30, no.3, pp.591-596.
- J.Zhang, R.Pu, J.Wang ,W.Huang, L.Yuan, J.Luo, “Detecting powdery mildew of winter wheat using leaf level hyper spectral measurements” Computers and Electronics in Agriculture, 2012, vol.85, pp. 13-23.
- L.Yuan, Y.Huang, R.W.Loraamm, et al, “Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects” Field Crops Research, 2014, vol.156, pp.199-207.
- J.Zhang, R.Pu, W.Huang, L.Yuan, J.Luo, J.Wang, “Using in-situ hyper spectral data for detecting and discriminating yellow rust disease from nutrient stresses” Field Crops Research, 2012, vol.134,pp.165-174.
- J .Zhang, L.Yuan, R.Pu, R.W.Loraamm, G.Yang, J .Wang, “Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat” Computers and Electronics in Agriculture, 2014, vol.100, pp.79-87.
- Y.K.Lee, W.T.Rhodes, “Nonlinear image processing by a rotating kernel transformation” Optics letters, 1990, vol.15, no.23, pp.1383-1385.
- S.B. Ullagaddi, Viswanadh Raju, “Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop” International Journal of Modern Education and Computer Science (IJMECS), Vol.9, No.1, pp.43-51, 2017.
