Abstract
In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this problem Omega, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. In this paper, we propose a new vision-based classification system for identifying weeds in vegetable fields such as spinach, beet and bean by applying convolutional neural networks (CNNs) and crop lines information. In this study, we combine deep learning with line detection to enforce the classification procedure. The proposed method is applied to high-resolution Unmanned Aerial Vehicles (UAV) images of vegetables taken about 20 m above the soil. We have performed an extensive evaluation of the method with real data. The results showed that the proposed method of weeds detection was effective in different crop fields. The overall precision for the beet, spinach and bean is respectively of 93%, 81% and 69%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
European Crop Protection: With or without pesticides? – ECPA (2017). http://www.ecpa.eu/with-or-without
Oerke, E.-C.: Crop losses to pests. J. Agric. Sci. 144(01), 31 (2006)
Pierce, F.J., Nowak, P.: Aspects of precision agriculture. Adv. Agron. 67(C), 1–85 (1999)
McBratney, A., Whelan, B., Ancev, T., Bouma, J.: Future directions of precision agriculture. Precis. Agric. 6(1), 7–23 (2005)
Torres-Sánchez, J., López-Granados, F., Peña, J.M.: An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Comput. Electron. Agric. 114, 43–52 (2015)
Zhang, C., Kovacs, J.M.: The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13(6), 693–712 (2012)
Peña Barragán, J.M., Kelly, M., de Castro, A.I., López Granados, F.: Object-based approach for crop row characterization in UAV images for site-specific weed management. In: Queiroz-Feitosa et al. (eds.) 4th International; Conference on Geographic Object-Based Image Analysis (GEOBIA 2012), Rio de Janeiro, Brazil, pp. 426–430 (2012)
Bah, M.D., Hafiane, A., Canals, R.: Weeds detection in UAV imagery using SLIC and the hough transform. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE, November 2017
Gée, C., Bossu, J., Jones, G., Truchetet, F.: Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60(1), 49–59 (2008)
Hamuda, E., Glavin, M., Jones, E.: A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184–199 (2016)
Pérez-Ortiz, M., Peña, J.M., Gutiérrez, P.A., Torres-Sánchez, J., Hervás-Martínez, C., López-Granados, F.: Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Syst. Appl. 47, 85–94 (2015)
Woebbecke, D., Meyer, G., Von Bargen, K., Mortensen, D.: Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38(1), 259–269 (1995)
Jeon, H.Y., Tian, L.F., Zhu, H.: Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors 11(6), 6270–6283 (2011)
Weis, M., Gerhards, R.: Detection of weeds using image processing and clustering. Bornimer Agrartechnische Berichte 69(2008), 138–144 (2008)
Latha, M., Poojith, A., Reddy, A., Kumar, V.: Image processing in agriculture. Int. J. Innovative Res. Electr. Electron. Instrum. Control Eng. 2(6), 2321 (2014)
Bakhshipour, A., Jafari, A., Nassiri, S.M., Zare, D.: Weed segmentation using texture features extracted from wavelet sub-images. Biosyst. Eng. 157, 1–12 (2017)
Ahmed, F., Al-Mamun, H.A., Bari, A.S.M.H., Hossain, E., Kwan, P.: Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot. 40, 98–104 (2012)
Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., Stachniss, C.: UAV-based crop and weed classification for smart farming. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3024–3031. IEEE, May 2017
Tang, L., Tian, L., Steward, B.L.: Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network. Trans. Am. Soc. Agric. Eng. 46(4), 1247–1254 (2003)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2323 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, pp. 1–9 (2012)
Hung, C., Xu, Z., Sukkarieh, S.: Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sens. 6(12), 12:037–12:054 (2014)
Milioto, A., Lottes, P., Stachniss, C.: Real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks. ISPRS Ann. Photogrammetry Remote Sens. Spat. Inf. Sci. IV-2/W3, 41–48 (2017)
Mortensen, A.K., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R., Gislum, R.: Semantic segmentation of mixed crops using deep convolutional neural network. In: CIGR-AgEng Conference (2016)
Pérez, A.J., López, F., Benlloch, J.V., Christensen, S.: Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 25(3), 197–212 (1997)
Kataoka, T., Kaneko, T., Okamoto, H., Hata, S.: Crop growth estimation system using machine vision. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), vol. 2, no. Aim, pp. 1079–1083 (2003)
Hague, T., Tillett, N.D., Wheeler, H.: Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 7(1), 21–32 (2006)
Meyer, G.E., Hindman, T., Laksmi, K.: Machine vision detection parameters for plant species identification. Proc. SPIE 3543, 327–335 (1998)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2011)
Acknowledgment
This work is part of the ADVENTICES project supported by the Centre-Val de Loire Region (France), grant number ADVENTICES 16032PR. We would like to thank the Centre-Val de Loire Region for supporting the work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bah, M.D., Dericquebourg, E., Hafiane, A., Canals, R. (2019). Deep Learning Based Classification System for Identifying Weeds Using High-Resolution UAV Imagery. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-01177-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01176-5
Online ISBN: 978-3-030-01177-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)