Abstract
Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a multispectral camera and collect data. However, the classification of multispectral images using supervised machine learning algorithms such as convolutional neural networks (CNN) requires a large amount of training data. This is a common drawback in deep learning. Our method makes use of a semi-supervised generative adversarial networks (GAN), providing a pixel-wise classification for all the acquired multispectral images. It consists of a generator network to provide photo-realistic images as extra training data to a multi-class classifier acting as a discriminator and trained on small amounts of labeled data. The performance of the proposed semi-supervised GAN is evaluated on the weedNet dataset consisting of multispectral crop and weed images collected by a micro aerial vehicle (MAV). Results indicate high classification accuracy can be achieved and show the potential of GAN-based methods for the challenging task of multispectral image classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., Stachniss, C.: UAV-based crop and weed classification for smart farming. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3024–3031 (2017)
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), 12037–12054 (2014)
Torres-Sánchez, J., Peña, J.M., de Castro, A.I., López-Granados, F.: Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 103, 104–113 (2014)
Herrera, P.J., Dorado, J., Ribeiro, A.: A novel approach for weed type classification based on shape descriptors and a fuzzy decision-making method. Sensors 14(8), 15304–15324 (2014)
Peña, J.M., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A.I., López-Granados, F.: Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15(3), 5609–5626 (2015)
Samseemoung, G., Soni, P., Jayasuriya, H.P., Salokhe, V.M.: Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation. Precision Agric. 13(6), 611–627 (2012)
López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A.I., Mesas-Carrascosa, F.J., Peña, J.M.: Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agric. 17(2), 183–199 (2016)
Bannari, A., Morin, D., Bonn, F., Huete, A.R.: A review of vegetation indices. Remote Sens. Rev. 13(1–2), 95–120 (1995)
Pena, J.M., Torres-Sánchez, J., de Castro, A.I., Kelly, M., López-Granados, F.: Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 8(10), e77151 (2013)
Louargant, M., Villette, S., Jones, G., Vigneau, N., Paoli, J.N., Gée, C.: Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images. Precision Agric. 18(6), 932–951 (2017)
Herrmann, I., Shapira, U., Kinast, S., Karnieli, A., Bonfil, D.J.: Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precision Agric. 14(6), 637–659 (2013)
Ishida, T., et al.: A novel approach for vegetation classification using UAV-based hyperspectral imaging. Comput. Electron. Agric. 144, 80–85 (2019)
Natividade, J., Prado, J., Marques, L.: Low-cost multi-spectral vegetation classification using an Unmanned Aerial Vehicle. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 336–342 (2017)
Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., Zhang, Y.: Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens. 10(7), 1119 (2018)
Sa, I., et al.: weedNet: dense semantic weed classification using multispectral images and MAV for smart farming. IEEE Robot. Autom. Lett. 3(1), 588–595 (2016)
Sa, I., et al.: WeedMap: a large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens. 10(9), 1423 (2018)
Bah, M.D., Dericquebourg, E., Hafiane, A., Canals, R.: Deep learning based classification system for identifying weeds using high-resolution UAV imagery. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) SAI 2018. AISC, vol. 857, pp. 176–187. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01177-2_13
Lottes, P., Behley, J., Milioto, A., Stachniss, C.: Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. In: arXiv preprint, arXiv:1806.03412 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: arXiv preprint, arXiv:1511.06434 (2015)
Acknowledgement
This work is part of the 5GRIT project supported by the Department for Digital, Culture, Media and Sport (DCMS), UK, through their 5G Testbeds Program.
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
Kerdegari, H., Razaak, M., Argyriou, V., Remagnino, P. (2019). Smart Monitoring of Crops Using Generative Adversarial Networks. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-29888-3_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29887-6
Online ISBN: 978-3-030-29888-3
eBook Packages: Computer ScienceComputer Science (R0)