Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier

Inacio Henrique Yano; Nelson Felipe Oliveros Mesa; Wesley Esdras Santiago; Rosa Helena Aguiar; Barbara Teruel

doi:doi:10.11648/j.jfns.20170506.11

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Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier

Inacio Henrique Yano, Nelson Felipe Oliveros Mesa, Wesley Esdras Santiago, Rosa Helena Aguiar, Barbara Teruel

Published in Journal of Food and Nutrition Sciences (Volume 5, Issue 6)

Received: 19 September 2017 Accepted: 30 September 2017 Published: 6 November 2017

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Abstract

The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.

Published in	Journal of Food and Nutrition Sciences (Volume 5, Issue 6)
DOI	10.11648/j.jfns.20170506.11
Page(s)	211-216
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Infestation, Machine Learning, Pattern Recognition, Remote Sensing

References

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[2]	Rudorff, B. F. T., Aguiar, D. A., Silva, W. F., Sugawara, L. M., Adami, M., & Moreira, M. A. (2010). Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using Landsat data. Remote sensing, 2(4), 1057-1076.
[3]	Firehun, Y., & Tamado, T. (2006). Weed flora in the Rift Valley sugarcane plantations of Ethiopia as influenced by soil types and agronomic practises. Weed biology and management, 6(3), 139-150.
[4]	Ferreira, E. A., Procópio, S. O., Galon, L., Franca, A. C., Concenço, G., Silva, A. A., & Rocha, P. R. R. (2010). Weed management in raw sugarcane. Planta Daninha, 28(4), 915-925.
[5]	Cerdeira, A. L., Paraíba, L. C., Queiroz, S. C. N. D., Matallo, M. B., & Ferracini, V. L. (2015). Estimation of herbicide bioconcentration in sugarcane (Saccharum officinarum L.). Ciência Rural, 45(4), 591-597.
[6]	Cavalli, R. M., Laneve, G., Fusilli, L., Pignatti, S., & Santini, F. (2009). Remote sensing water observation for supporting Lake Victoria weed management. Journal of environmental management, 90(7), 2199-2211.
[7]	Holben, B. N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International journal of remote sensing, 7(11), 1417-1434.
[8]	Peña, J. M., Torres-Sánchez, J., de Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) pictures. PLoS One, 8(10), e77151.
[9]	Chen, Y., P. Lin, Y. He and Z Xu. 2011. Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds. Biosystems engr., 09(3): 220-227.
[10]	Rahman, M., Blackwell, B., Banerjee, N., & Saraswat, D. (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture, 113, 14-23.
[11]	Christensen, S., Søgaard, H. T., Kudsk, P., Nørremark, M., Lund, I., Nadimi, E. S., & Jørgensen, R. (2009). Site‐specific weed control technologies. Weed Research, 49(3), 233-241.
[12]	Metre, V., & Ghorpade, J. (2013). An overview of the research on texture based plant leaf classification. arXiv preprint arXiv: 1306.4345.
[13]	Dixit, A., Hegde, N., & Hiremath, P. S. (2016). Cluster Analysis of Satellite (LISS-III) Pictures of Earth surface.
[14]	Vieira, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5), 360-363.
[15]	Ma, L., M. M. Crawford and J. Tian. 2010. Local manifold learning-based-nearest-neighbor for hyperspectral image classification. IEEE Trans. Geoscience Remote Sens. (TGRS), 48(11): 4099-4109.
[16]	Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.

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APA Style

Inacio Henrique Yano, Nelson Felipe Oliveros Mesa, Wesley Esdras Santiago, Rosa Helena Aguiar, Barbara Teruel. (2017). Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. Journal of Food and Nutrition Sciences, 5(6), 211-216. https://doi.org/10.11648/j.jfns.20170506.11

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ACS Style

Inacio Henrique Yano; Nelson Felipe Oliveros Mesa; Wesley Esdras Santiago; Rosa Helena Aguiar; Barbara Teruel. Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. J. Food Nutr. Sci. 2017, 5(6), 211-216. doi: 10.11648/j.jfns.20170506.11

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AMA Style

Inacio Henrique Yano, Nelson Felipe Oliveros Mesa, Wesley Esdras Santiago, Rosa Helena Aguiar, Barbara Teruel. Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. J Food Nutr Sci. 2017;5(6):211-216. doi: 10.11648/j.jfns.20170506.11

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@article{10.11648/j.jfns.20170506.11,
  author = {Inacio Henrique Yano and Nelson Felipe Oliveros Mesa and Wesley Esdras Santiago and Rosa Helena Aguiar and Barbara Teruel},
  title = {Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier},
  journal = {Journal of Food and Nutrition Sciences},
  volume = {5},
  number = {6},
  pages = {211-216},
  doi = {10.11648/j.jfns.20170506.11},
  url = {https://doi.org/10.11648/j.jfns.20170506.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfns.20170506.11},
  abstract = {The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.},
 year = {2017}
}

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TY  - JOUR
T1  - Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier
AU  - Inacio Henrique Yano
AU  - Nelson Felipe Oliveros Mesa
AU  - Wesley Esdras Santiago
AU  - Rosa Helena Aguiar
AU  - Barbara Teruel
Y1  - 2017/11/06
PY  - 2017
N1  - https://doi.org/10.11648/j.jfns.20170506.11
DO  - 10.11648/j.jfns.20170506.11
T2  - Journal of Food and Nutrition Sciences
JF  - Journal of Food and Nutrition Sciences
JO  - Journal of Food and Nutrition Sciences
SP  - 211
EP  - 216
PB  - Science Publishing Group
SN  - 2330-7293
UR  - https://doi.org/10.11648/j.jfns.20170506.11
AB  - The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.
VL  - 5
IS  - 6
ER  -

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Author Information

Inacio Henrique Yano

Faculty of Agricultural Engineering, University of Campinas, Campinas, Brazil; Embrapa Informatica Agropecuaria, Campinas, Brazil
Nelson Felipe Oliveros Mesa

Faculty of Agricultural Engineering, University of Campinas, Campinas, Brazil
Wesley Esdras Santiago

Institute of Agricultural Sciences, Federal University of the Jequitinhonha and Mucuri Valleys, Unai, Brazil
Rosa Helena Aguiar

Faculty of Agricultural Engineering, University of Campinas, Campinas, Brazil
Barbara Teruel

Faculty of Agricultural Engineering, University of Campinas, Campinas, Brazil

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APA Style

Inacio Henrique Yano, Nelson Felipe Oliveros Mesa, Wesley Esdras Santiago, Rosa Helena Aguiar, Barbara Teruel. (2017). Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. Journal of Food and Nutrition Sciences, 5(6), 211-216. https://doi.org/10.11648/j.jfns.20170506.11

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ACS Style

Inacio Henrique Yano; Nelson Felipe Oliveros Mesa; Wesley Esdras Santiago; Rosa Helena Aguiar; Barbara Teruel. Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. J. Food Nutr. Sci. 2017, 5(6), 211-216. doi: 10.11648/j.jfns.20170506.11

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AMA Style

Inacio Henrique Yano, Nelson Felipe Oliveros Mesa, Wesley Esdras Santiago, Rosa Helena Aguiar, Barbara Teruel. Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier. J Food Nutr Sci. 2017;5(6):211-216. doi: 10.11648/j.jfns.20170506.11

Copy | Download

@article{10.11648/j.jfns.20170506.11,
  author = {Inacio Henrique Yano and Nelson Felipe Oliveros Mesa and Wesley Esdras Santiago and Rosa Helena Aguiar and Barbara Teruel},
  title = {Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier},
  journal = {Journal of Food and Nutrition Sciences},
  volume = {5},
  number = {6},
  pages = {211-216},
  doi = {10.11648/j.jfns.20170506.11},
  url = {https://doi.org/10.11648/j.jfns.20170506.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfns.20170506.11},
  abstract = {The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier
AU  - Inacio Henrique Yano
AU  - Nelson Felipe Oliveros Mesa
AU  - Wesley Esdras Santiago
AU  - Rosa Helena Aguiar
AU  - Barbara Teruel
Y1  - 2017/11/06
PY  - 2017
N1  - https://doi.org/10.11648/j.jfns.20170506.11
DO  - 10.11648/j.jfns.20170506.11
T2  - Journal of Food and Nutrition Sciences
JF  - Journal of Food and Nutrition Sciences
JO  - Journal of Food and Nutrition Sciences
SP  - 211
EP  - 216
PB  - Science Publishing Group
SN  - 2330-7293
UR  - https://doi.org/10.11648/j.jfns.20170506.11
AB  - The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.
VL  - 5
IS  - 6
ER  -

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