Identification of peach leaf disease infected by Xanthomonas campestris with deep learning

Engineering in Agriculture, Environment and Food - Tập 12 - Trang 388-396 - 2019
Keke Zhang1, Zheyuan Xu2, Shoukun Dong3, Canjian Cen1, Qiufeng Wu2
1College of Engineering, Northeast Agricultural University, Harbin, 150030, China
2College of Science, Northeast Agricultural University, Harbin, 150030, China
3College of Agriculture, Northeast Agricultural University, Harbin 150030, China

Tài liệu tham khảo

Akbani, 2004, Applying support vector machines to imbalanced datasets, vol. 3201, 39 Alvarez, 1994, Serological, pathological, and genetic diversity among strains of Xanthomonas campestris infecting crucifers, Phytopathology, 84, 1449, 10.1094/Phyto-84-1449 Anthimopoulos, 2016, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Trans. Med. Imag., 35, 1207, 10.1109/TMI.2016.2535865 Bengio, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579 Cao, 2013, An optimized cost-sensitive SVM for imbalanced data learning, 280 Chawla, 2004, Editorial: special issue on learning from imbalanced data sets, SIGKDD Explor. Spec. Issue Lear. Imbalanced Datasets, 6, 1, 10.1145/1007730.1007733 Cheng, 2017, Pest identification via deep residual learning in complex background, Comput. Electron. Agric., 141, 351, 10.1016/j.compag.2017.08.005 Dyrmann, 2016, Plant species classification using deep convolutional neural network, Biosyst. Eng., 151, 72, 10.1016/j.biosystemseng.2016.08.024 Fawcett, 2016, An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861, 10.1016/j.patrec.2005.10.010 Ghaz, 2017, Plant identification using deep neural networks via optimization of transfer learning parameters, Neurocomputing, 235, 228, 10.1016/j.neucom.2017.01.018 Glorot, 2011, Deep sparse rectifier neural networks, vol. 15, 315 Guan, 2016, Multi-type feature fusion technique for weed identification in cotton fields, Int. J. Signal Process. Image Process. Pattern Recogn., 9, 355 Henson, 1993, The polymerase chain reaction and plant disease diagnosis, Ann. Rev. Phytopathol., 31, 81, 10.1146/annurev.py.31.090193.000501 Hughes, 2015 Kailey, 2012, Content-based image retrieval (CBIR) for identifying image based plant disease, Int. J. Comput. Technol. Appl.(2229-6093), 3, 1099 Koo, 2013, Development of a real-time microchip PCR system for portable plant disease diagnosis, PloS One, 8, 10.1371/journal.pone.0082704 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Lazebnik, 2006, vol. 2, 2169 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lee, 2017, vol. 71, 1 Li, 2011, Actions in still web images: visualization, detection and retrieval, 302 Liu, 2013, A novel k-nearest neighbor algorithm based on i-divergence criterion, ICIC Express Lett. Part B Appl. Int. J. Res. Surv., 4, 243 Mao, 2007, Determination of thiodiazole-copper residue in watermelon by HPLC, J. Instrum. Anal., 26, 752 Mehdipour, 2017, Plant identification using deep neural networks via optimization of transfer learning parameters, Neurocomputing, 235, 228, 10.1016/j.neucom.2017.01.018 Melendez, 2015, A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest x-rays, IEEE Trans. Med. Imaging, 34, 179, 10.1109/TMI.2014.2350539 Mohanty, 2016, Using deep learning for image-based plant disease detection, Front. Plant Sci., 7, 1, 10.3389/fpls.2016.01419 Nair, 2010, Rectified linear units improve restricted Boltzmann machines, 807 Olgun, 2016, Wheat grain classification by using dense SIFT features with SVM classifier, Comput. Electron. Agric., 122, 185, 10.1016/j.compag.2016.01.033 Patil, 2014, Classification of cotton leaf spot disease using support vector machine, Int. J. Eng. Res. Appl., 4, 92 Provost, 2001, Robust classification for imprecise environments, Mach. Learn., 42, 203, 10.1023/A:1007601015854 Revathi, 2012, Homogenous segmentation based edge detection techniques for proficient identification of the cotton leaf spot diseases, Int. J. Comput. Technol. Appl.(0975-8887), 47, 18 Ruder, 2017 Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y Sasaki, 1998, 145 Shen, 2001, Action mode of bismerthiazol against rice leaf blight, Chin. J. Pestic. Sci., 3, 35 Sun, 2017, Deep learning for plant identification in natural environment, Comput. Intell. Neurosci., 4, 1 Sun, 2016, Hybrid deep learning for face verification, IEEE Trans. Pattern Anal. Mach. Intell., 38, 1997, 10.1109/TPAMI.2015.2505293 Swets, 2000, Better decisions through science, Sci. Am., 283, 82, 10.1038/scientificamerican1000-82 Tang, 2017, Weed identification based on K-means feature learning combined with convolutional neural network, Comput. Electron. Agric., 135, 63, 10.1016/j.compag.2017.01.001 Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302 Vicente, 2013, Xanthomonas campestris pv. campestris (cause of black rot of crucifers) in the genomic era is still a worldwide threat to brassica crops, Mol. Plant Pathol., 14, 2, 10.1111/j.1364-3703.2012.00833.x Wang, 2008, The residue and field residue decline study of 20% hypertomic ethylicin EC in plant, rice, rice hull, paddy water and soil, Chin. J. Pesticides Sci., 10, 455 Wu, 2014, A method of target detection for crop disease spots by improved Hough transform, Trans. Chin. Soc. Agric. Eng., 30, 152 Xie, 2017, Deep learning in visual computing and signal processing, Appl. Comput. Intell. Soft Comput., 10, 1, 10.1155/2017/1320780 Xu, 2013, A novel K-nearest neighbor classification algorithm based on maximum entropy, Int. J. Adv. Comput. Technol., 5, 966 Yan, 2016, Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition, IEEE Trans. Med. Imaging, 35, 1332, 10.1109/TMI.2016.2524985 Zeiler, 2014, 818 Zhang, 2016, Road crack detection using deep convolutional neural network, 3708