Vision-based plant disease detection system using transfer and deep learning

2017 ASABE Annual International Meeting

Volumn , Issue , 2017, Pages

  Cruz, Albert C ^a   Luvisi, Andrea ^b   De Bellis, Luigi ^b   Ampatzidis, Yiannis ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF SALENTO   (Italy)

Author keywords

Convolutional neural networks; Deep learning; Machine vision; Olea europaea L.; Transfer learning; Xylella fastidiosa

Indexed keywords

BACTERIA; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPUTER VISION; CONVOLUTION; DIAGNOSIS; NEURAL NETWORKS; PLANTS (BOTANY); STOCHASTIC SYSTEMS;

CONVOLUTIONAL NEURAL NETWORK; LEVELS OF ABSTRACTION; OLEA EUROPAEA L; QUANTITATIVE DETECTION; STOCHASTIC GRADIENT DESCENT METHOD; TRANSFER LEARNING; TRUE POSITIVE RATES; XYLELLA FASTIDIOSA;

DEEP LEARNING;

EID: 85020648288     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.13031/aim.201700241     Document Type: Conference Paper

References (37)

1. Almaev, T. R., & Valstar, M. F. (2013). Local gabor binary patterns from three orthogonal planes for automatic facial expression recognition. Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013, 356-361. https://doi.org/10.1109/ACII.2013.65.
2. Baxi, A., & Vala, H. (2013). A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering \& Technology (IJARCET), 2(2), 387.
3. Brachmann, A., & Redies, C. (2016). Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images. Symmetry, 8(12), 144. https://doi.org/10.3390/sym8120144.
4. Chang, C.-C, & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. https://doi.org/10.1145/1961189.1961199.
5. Chen, J., Chen, Z., Chi, Z., & Fu, H. (2014). Emotion recognition in the wild with feature fusion and multiple kernel learning. In ACM Intl. Conf. Multimodal Interaction Workshops (pp. 508-513).
6. Ciresan, D. C, Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011). Flexible, high performance convolutional neural networks for image classification. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1237-1242). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210.
7. Cruz, A. C, Bhanu, B., & Thakoor, N. S. (2015). Background suppressing Gabor energy filtering. Pattern Recognition Letters, 52, 40-47. https://doi.org/10.1016/j.patrec.2014.10.001.
8. Cunha, J. B. (2003). Application of image processing techniques in the characterization of plant leafs. In 2003 IEEE International Symposium on Industrial Electronics (pp. 612-616). IEEE. https://doi.org/10.1109/ISIE.2003.1267322
9. Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.
10. Food and Agriculture Organization of the United Nation's Crop Data. (2017).
11. Fujita, E., Kawasaki, Y, Uga, H., Kagiwada, S., & Iyatomi, H. (2016). Basic investigation on a robust and practical plant diagnostic system. In Proceedings of the 2016 IEEE Machine Learning and its Applications (pp. 989-992). https://doi.org/10.1109/ICMLA.2016.56.
12. Grigorescu, C, Petkov, N, & Westenberg, M. A. (2003). Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing, 12(7), 729-739. https://doi.org/10.1109/TIP.2003.814250.
13. Haiyan Song, & Yong He. (2005). Crop Nutrition Diagnosis Expert System Based on Artificial Neural Networks. In Third International Conference on Information Technology and Applications (ICITA'05) (Vol. 1, pp. 357-362). IEEE. https://doi.org/10.1109/ICITA.2005.108.
14. Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8, 179-187.
15. International Olive Council's World Olive Oil Figures. (2013).
16. Luvisi, A., Ampatzidis, Y, & De Bellis, L. (2016). Plant Pathology and Information Technology: Opportunity for Management of Disease Outbreak and Applications in Regulation Frameworks. Sustainability 2016, 8(8), 831; doi: 10.3390/su8080831.
17. Karpathy, A., Toderici, G, Shetty, S., Leung, T, Sukthankar, R., & Li, F F (2014). Large-scale video classification with convolutional neural networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1725-1732. https://doi.org/10.1109/CVPR.2014.223.
18. Krizhevsky, A., Sutskever, I., & Hinton, G E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems, 1-9. https://doi.org/http://dx.doi.org/10.1016/j.protcy.2014.09.007.
19. Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. B. (2012). Leafsnap: A computer vision system for automatic plant species identification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7573 LNCS (PART 2), 502-516. https://doi.org/10.1007/978-3-642-33709-3-36.
20. LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 3361(10), 255-258. https://doi.org/10.1109/IJCNN.2004.1381049.
21. Liu, C., Yuen, J., & Torralba, A. (2015). Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(5), 15-49. https://doi.org/10.1007/978-3-319-23048-1-2.
22. Lyons, M., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with Gabor wavelets. In Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 (pp. 200-205). https://doi.org/10.1109/AFGR.1998.670949.
23. Mohanty, S. P., Hughes, D., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Front. Plant Sci., 7, 1419. https://doi.org/10.3389/fpls.2016.01419.
24. Munisami, T., Ramsurn, M., Kishnah, S., & Pudaruth, S. (2015). Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers. In Procedia Computer Science (Vol. 58, pp. 740-747). https://doi.org/10.1016/j.procs.2015.08.095.
25. Nilsback, M. E., & Zisserman, A. (2008). Automated flower classification over a large number of classes. Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008, 722-729. https://doi.org/10.1109/ICVGIP.2008.47.
26. Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition. In International Conference on Pattern Recognition Applications and Methods.
27. PlantVillage. (n. d.). Retrieved April 25, 2017, from https://www.plantvillage.org/.
28. Rinaldi, A., & Cruz, A. C. (2016). Deep Context Injection for Super-resolution. In International Conference on Image Processing, Computer Vision and Pattern Recognition (pp. 353-354).
29. Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1, 1-14. https://doi.org/10.1016/j.infsof.2008.09.005.
30. Söderkvist, O. J. O. (2001). Computer vision classification of leaves from swedish trees. Linkoping University.
31. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.,⋯ Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 1-9. https://doi.org/10.1109/CVPR.2015.7298594.
32. Vapnik, V. (2015). Learning Using Privileged Information?: Similarity Control and Knowledge Transfer. Journal of Machine Learning Research, 16, 2023-2049. https://doi.org/10.1007/978-3-319-17091-6-1.
33. Vapnik, V., & Vashist, A. (2009). A new learning paradigm: Learning using privileged information. Neural Networks, 22(5-6), 544-557. https://doi.org/10.1016/j.neunet.2009.06.042.
34. Wiegand, C. L., Richardson, A. J., Jackson, R. D., Pinter, P. J., Aase, J. K., Smika, D. E.,⋯ McMurtrey, J. E. (1986). Development of Agrometeorological Crop Model Inputs from Remotely Sensed Information. IEEE Transactions on Geoscience and Remote Sensing, GE-24(1), 90-98.
35. Xiao, X. Y., Hu, R., Zhang, S. W., & Wang, X. F. (2010). HOG-based approach for leaf classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6216, 149-155. https://doi.org/10.1007/978-3-642-14932-0-19.
36. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems 27(Proceedings of NIPS), 27, 1-9. Retrieved from http://arxiv.org/abs/1411.1792.
37. Zhenjiang, M. (2000). Zernike moment-based image shape analysis and its application. Pattern Recognition Letters (Vol. 21). https://doi.org/10.1016/S0167-8655 (99) 00144-0.