Classification of weed in soybean crops using unmanned aerial vehicle images
Resumo
Soybeans have been Brazil's main agricultural commodity, contributing substantially to the country's trade balance. However, their production and productivity costs are affected by weeds, diseases and pests. This paper proposes a computer vision system to monitor weeds in soybean fields using images captured by a UAV. The proposed system adopts the SLIC superpixels segmentation method to detect the plants in the images and visual attributes to describe the characteristics of the physical properties of the leaf, such as color, gradient, texture and shape. Our methodology evaluated the performance of three classifiers (kNN, Randon Forest and SVM) for images captured at a height of 3 meters. The best results were obtained by the SVM classification algorithm with accuracy of 91.34%. However, the results do not yet indicate that our approach can support experts and farmers in weed monitoring in soybean crops, requiring more images and experiments.
Palavras-chave:
classification, weeds, soybean crop, UAV, images
Referências
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R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection. Lawrence Erlbaum Associates, vol. 14, p. 1137 - 1145, 1995.
M. A. Rizzardi and N. G. Fleck, Métodos de quantificação da cobertura foliar da infestação de plantas daninhas e da cultura da soja, Ciência rural. Santa Maria. Vol. 34, n. 1 (jan./fev. 2004), p. 13 - 18, 2004. DOI: 10.1590/s0103-84782004000100003
T. Brosnan and D.-W. Sun, Inspection and grading of agricultural and food products by computer vision systems-a review, Computers and electronics in agriculture, vol. 36, no. 2-3, pp. 193 - 213, 2002. DOI: 10.1016/s0168-1699(02)00101-1
S. Finch, A. Samuel, and G. P. Lane, Lockhart and Wiseman's Crop Husbandry Including Grassland. Elsevier, 2014.
F. Garcia-Ruiz, S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen, and R. Ehsani, Comparison of two aerial imaging platforms for identification of huanglongbing-infected citrus trees, Computers and Electronics in Agriculture, vol. 91, pp. 106 - 115, 2013. DOI: 10.1016/j.compag.2012.12.002
J. Torres-Sánchez, F. López-Granados, A. I. De Castro, and J. M. Peña-Barragán, Configuration and specifications of an unmanned aerial vehicle (uav) for early site specific weed management, PloS one, vol. 8, no. 3, p. e58210, 2013. DOI: 10.1371/journal.pone.0058210
J. Torres-Sánchez, J. M. Peña, A. I. de Castro, and F. López-Granados, Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from uav, Computers and Electronics in Agriculture, vol. 103, pp. 104 - 113, 2014. DOI: 10.1016/j.compag.2014.02.009
J. M. Peña, J. Torres-Sánchez, A. I. de Castro, M. Kelly, and F. López-Granados, Weed mapping in early-season maize fields using objectbased analysis of unmanned aerial vehicle (uav) images, PloS one, vol. 8, no. 10, p. e77151, 2013. DOI: 10.1371/journal.pone.0077151
M. Pérez-Ortiz, J. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. HervásMartínez, and F. López-Granados, A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method, Applied Soft Computing, vol. 37, pp. 533 - 544, 2015. DOI: 10.1016/j.asoc.2015.08.027
M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, Selecting patterns and features for between-and within-crop-row weed mapping using uavimagery, Expert Systems with Applications, vol. 47, pp. 85 - 94, 2016. DOI: 10.1016/j.eswa.2015.10.043
F. López-Granados, J. Torres-Sánchez, A. Serrano-Pérez, A. I. de Castro, F.-J. Mesas-Carrascosa, and J. -M. Pena, Early season weed mapping in sunflower using uav technology: variability of herbicide treatment maps against weed thresholds, Precision Agriculture, vol. 17, no. 2, pp. 183 - 199, 2016. DOI: 10.1007/s11119-015-9415-8
F. Castaldi, F. Pelosi, S. Pascucci, and R. Casa, Assessing the potential of images from unmanned aerial vehicles (uav) to support herbicide patch spraying in maize, Precision Agriculture, vol. 18, no. 1, pp. 76 - 94, 2017. DOI: 10.1007/s11119-016-9468-3
J. A. Hartigan and M. A. Wong, Algorithm as 136: A k-means clustering algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100 - 108, 1979. DOI: 10.2307/2346830
M. J. Swain and D. H. Ballard, Color indexing, International journal of computer vision, vol. 7, no. 1, pp. 11 - 32, 1991. DOI: 10.1007/bf00130487
R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol. 67, no. 5, pp. 786 - 804, 1979. DOI: 10.1109/proc.1979.11328
T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971 - 987, 2002. [Online]. Available: http://dx.doi.org/10.1109/TPAMI. 2002.1017623 DOI: 10.1109/tpami.2002.1017623
N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in international Conference on computer vision & Pattern Recognition (CVPR'05), vol. IEEE Computer Society, 2005, pp. 886 - 893. DOI: 10.1109/cvpr.2005.177
M. K. Hu, Visual Pattern Recognition by Moment Invariants, IRE Transactions on Information Theory, vol. 8, pp. 179 - 187, 1962. DOI: 10.1109/tit.1962.1057692
T. K. Ho, Random decision forests, in Proceedings of 3rd international conference on document analysis and recognition, vol. IEEE, 1995, pp. 278 - 282. DOI: 10.1109/icdar.1995.598994
N. S. Altman, An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, vol. 46, no. 3, pp. 175 - 185, 1992. DOI: 10.2307/2685209
J. Platt et al., Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in large margin classifiers, vol. 10, no. 3, pp. 61 - 74, 1999.
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection. Lawrence Erlbaum Associates, vol. 14, p. 1137 - 1145, 1995.
Publicado
09/09/2019
Como Citar
BELETE, Nícolas Alessandro de Souza; TETILA, Everton Castelão; ASTOLFI, Gilberto; PISTORI, Hemerson.
Classification of weed in soybean crops using unmanned aerial vehicle images. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 121-125.
DOI: https://doi.org/10.5753/wvc.2019.7639.
