The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
Abstract
Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the normalized difference vegetation index as a distinguishable feature for automatic ground truth generation. We train six models with different numbers of input channels and condition (fine tune) it to achieve ~0.8 F1-score and 0.78 area under the curve classification metrics. For the model deployment, an embedded Graphics Processing Unit (GPU) system (Jetson TX2) is tested for MAV integration. Dataset used in this letter is released to support the community and future work.
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Cites
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Research works printed but not linked: 6
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Cited by
Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E.-H. M. (2019) · IEEE Access · doi:10.1109/access.2019.2932609 · Cited by 1,306
Crop yield prediction with deep convolutional neural networks
Nevavuori, P., Narra, N., & Lipping, T. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104859 · Cited by 586
A survey of deep learning techniques for weed detection from images
Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106067 · Cited by 478
A survey of public datasets for computer vision tasks in precision agriculture
Lu, Y., & Young, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105760 · Cited by 382
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
Metadata
- Authors
- Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-01
- Volume
- 3
- Issue
- 1
- Pages
- 588-595
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2755766995
- Cited by
- 369
- References
- 36
- FWCI
- 23.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Funders
- European Union's Horizon 2020 Research and Innovation Programme (644227, 644128)
- Swiss State Secretariat for Education, Research and Innovation (15.0029, 15.0044)
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- Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2018). weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2017.2774979
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