Abstract
A challenge in precision agriculture is the detection of pests in agricultural environments. This paper describes a methodology to detect the presence of the nematode pest in coffee crops. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a commercial coffee plantation. The proposed methodology enables the extraction of visual features from image regions and uses supervised machine learning (ML) techniques to classify areas into two classes: pests and non-pests. Several learning techniques were compared using approaches with and without segmentation. Results demonstrate the methodology potential, with an average for the f-measure of 63% for Convolutional Neural Network (U-net) with manual segmentation.
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References
Food and agriculture organization of the united nations (2018). http://faostat.fao.org
Boisseau, M., Aribi, J., Sousa, F.R., Carneiro, R.M.D.G., Anthony, F.: Resistance to meloidogyne paranaensis in wild coffea arabica. Trop. Plant Pathol. 34, 38–41 (2009)
Breiman, L.: Random forests. Mach. Learn. 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Calderon, R., Montes-Borrego, M., Landa, B., Navas-Cortés, J.A., Zarco-Tejada, P.J.: Detection of downy mildew of opium poppy using high-resolution mult-spectral and thermal imagery acquired with an unmanned aerial vehicle. Prec. Agric. 15, 639–661 (2014)
Calderon, R., Navas-Cortes, J.A., Lucena, C., Zarco-Tejada, P.J.: High-resolution airborne hyperspectral and thermal imagery for early detection of verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 139, 231–245 (2013)
Campos, V.P., Villain, L.: Plant Parasitic Nematodes in Subtropical and Tropical Agriculture. CAB International, Wallingford (2005)
Garcia-Ruiz, F., Sankaran, S., Maja, J.M., Lee, W.S., Rasmussen, J., Ehsani, R.: Comparison of two aerial imaging platforms for identification of huanglongbing-infected citrus trees. Comput. Electron. Agric. 91, 106–115 (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)
Otoboni, C.E.M., Gabia, A., Martins, A.S.: Determinação de áreas para o manejo localizado de nematoides em soja com o uso de imagens de vant. In: Congresso Brasileiro de Nematologia (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 12th International Conference on Computer Vision Workshops, pp. 1393–1400 (2009)
Salgado, S.M.L., Rezende, J.C., Nunes, J.A.R.: Selection of coffee progenies for resistance to nematode meloidogyne paranaensis in infested area. Crop Breed. Appl. Biotechnol. 14, 94–101 (2014)
Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). The MIT Press, Cambridge (2006)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Souza, J.R., Mendes, C.C., Guizilini, V., Vivaldini, K.C., Colturato, A., Ramos, F., Wolf, D.F.: Automatic detection of ceratocystis wilt in eucalyptus crops from aerial images. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 3443–3448. IEEE (2015)
Acknowledgements
The Titan Xp used for this research was donated by the NVIDIA Corporation. This work was supported by the Federal University of Uberlandia, CNPq scholarship (process number 163641/2018-8) and CNPq under Grant 400699/2016-8.
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Oliveira, A.J., Assis, G.A., Guizilini, V., Faria, E.R., Souza, J.R. (2019). Segmenting and Detecting Nematode in Coffee Crops Using Aerial Images. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_25
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