Automatic detection of thistle-weeds in cereal crops from aerial RGB images

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

Automatic detection of thistle-weeds in cereal crops from aerial RGB images. / Franco, Camilo; Guada, Carely; Rodríguez, J. Tinguaro; Nielsen, Jon; Rasmussen, Jesper; Gómez, Daniel; Montero, Javier.

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings. Springer, 2018. p. 441-452 (Communications in Computer and Information Science, Vol. 855).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Franco, C, Guada, C, Rodríguez, JT, Nielsen, J, Rasmussen, J, Gómez, D & Montero, J 2018, Automatic detection of thistle-weeds in cereal crops from aerial RGB images. in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings. Springer, Communications in Computer and Information Science, vol. 855, pp. 441-452, 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018, Cadiz, Spain, 11/06/2018. https://doi.org/10.1007/978-3-319-91479-4_37

APA

Franco, C., Guada, C., Rodríguez, J. T., Nielsen, J., Rasmussen, J., Gómez, D., & Montero, J. (2018). Automatic detection of thistle-weeds in cereal crops from aerial RGB images. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings (pp. 441-452). Springer. Communications in Computer and Information Science Vol. 855 https://doi.org/10.1007/978-3-319-91479-4_37

Vancouver

Franco C, Guada C, Rodríguez JT, Nielsen J, Rasmussen J, Gómez D et al. Automatic detection of thistle-weeds in cereal crops from aerial RGB images. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings. Springer. 2018. p. 441-452. (Communications in Computer and Information Science, Vol. 855). https://doi.org/10.1007/978-3-319-91479-4_37

Author

Franco, Camilo ; Guada, Carely ; Rodríguez, J. Tinguaro ; Nielsen, Jon ; Rasmussen, Jesper ; Gómez, Daniel ; Montero, Javier. / Automatic detection of thistle-weeds in cereal crops from aerial RGB images. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings. Springer, 2018. pp. 441-452 (Communications in Computer and Information Science, Vol. 855).

Bibtex

@inproceedings{13748f48cbd94941b0f87a78210e9d14,
title = "Automatic detection of thistle-weeds in cereal crops from aerial RGB images",
abstract = "Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.",
keywords = "Image analysis, k-Nearest neighbours, Neural networks, Precision agriculture, Soft measures, Weed detection",
author = "Camilo Franco and Carely Guada and Rodr{\'i}guez, {J. Tinguaro} and Jon Nielsen and Jesper Rasmussen and Daniel G{\'o}mez and Javier Montero",
year = "2018",
doi = "10.1007/978-3-319-91479-4_37",
language = "English",
isbn = "9783319914787",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "441--452",
booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings",
address = "Switzerland",
note = "17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018 ; Conference date: 11-06-2018 Through 15-06-2018",

}

RIS

TY - GEN

T1 - Automatic detection of thistle-weeds in cereal crops from aerial RGB images

AU - Franco, Camilo

AU - Guada, Carely

AU - Rodríguez, J. Tinguaro

AU - Nielsen, Jon

AU - Rasmussen, Jesper

AU - Gómez, Daniel

AU - Montero, Javier

PY - 2018

Y1 - 2018

N2 - Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.

AB - Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.

KW - Image analysis

KW - k-Nearest neighbours

KW - Neural networks

KW - Precision agriculture

KW - Soft measures

KW - Weed detection

U2 - 10.1007/978-3-319-91479-4_37

DO - 10.1007/978-3-319-91479-4_37

M3 - Article in proceedings

AN - SCOPUS:85048054602

SN - 9783319914787

T3 - Communications in Computer and Information Science

SP - 441

EP - 452

BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Proceedings

PB - Springer

T2 - 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018

Y2 - 11 June 2018 through 15 June 2018

ER -

ID: 201043385