Abstract
Weed identification is a fundamental step in weed management. Traditional identification based on taxonomic features can be extremely challenging, especially at young seedling stage. It could also take days or months to confirm the identification through various channels, which would mean the loss of prime opportunity to control the weed. Recent advances in computer vision and machine learning have shown great success in various automatic visual detection tasks. It is therefore appropriate choice to capture visual field information and further process it to be able to realize autonomous weed identification promptly. This paper presents a convenient approach that applies image processing and machine learning for quick and accurate weeds identification on-site. Three deep models have been implemented to identify weeds via a smartphone. It is a proof-of-concept study targeting 16 selected most important agricultural weeds in Australia. We believe the proposed approach can help growers make a timely decision to spray the corresponding herbicide to reduce the financial loss annually.
Supported by Charles Sturt University research fund.
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
References
Llewellyn, R., et al.: Impact of weeds in Australian grain production. Grains Research and Development Corporation, Canberra, ACT, Australia (2016)
Ag Weed ID. http://www.farms.com/agriculture-apps/crops/ag-weed-id
BASF Weed ID. https://itunes.apple.com/au/app/weed-id/id506639384?mt=8
Alaska Weeds ID. https://apps.bugwood.org/apps/alaska/
Environmental Weeds of Sydney. https://itunes.apple.com/au/app/environmental-weeds-of-sydney/id914766739?mt=8
GRDC Weeds ID UTE Guide. https://grdc.com.au/resources-and-publications/apps/weed-id-the-ute-guide
Garden Answers Plant ID. http://www.gardenanswers.com/
Plantsnap. https://plantsnap.com
Pl@ntNet. https://plantnet.org/en
ForestXplorer app. https://www.forestry.gov.uk/mobileapp
De Rainville, F.-M., et al.: Bayesian classification and unsupervised learning for isolating weeds in row crops. Pattern Anal. Appl. 17(2), 401–414 (2014)
Haug, S., et al.: Plant classification system for crop/weed discrimination without segmentation. In: IEEE Winter Conference on Applications of Computer Vision. IEEE (2014)
Kounalakis, T., Triantafyllidis, G.A., Nalpantidis, L.: Weed recognition framework for robotic precision farming. In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE (2016)
Saha, D., Hanson, A., Shin, S.Y.: Development of enhanced weed detection system with adaptive thresholding and support vector machine. In: Proceedings of the International Conference on Research in Adaptive and Convergent Systems (2016)
Murawwat, S., et al.: Weed detection using SVMs. Eng. Technol. Appl. Sci. Res. 8(1), 2412–2416 (2018)
Fulwood, J.: Weed detection technology offers new approach (2019). https://groundcover.grdc.com.au/story/6009959/no-place-to-hide-for-in-crop-weeds/
Wu, Q., Zhang, K., Meng, J.: Identification of soybean leaf diseases via deep learning. J. Inst. Eng. (India): Series A 100(4), 659–666 (2019). https://doi.org/10.1007/s40030-019-00390-y
Wang, Q., et al.: Identification of tomato disease types and detection of infected areas based on deep convolutional neural networks and object detection techniques. Comput. Intell. Neurosci. 2019 (2019)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7(1419) (2016)
Ramcharan, A., et al.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017)
Ma, J., et al.: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput. Electron. Agric. 154, 18–24 (2018)
Geetharamani, G., Pandian, A.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Electr. Eng. 76, 323–338 (2019)
Ding, H., et al.: Study on identification for the typical pasture based on image texture features. In: 2019 Chinese Control and Decision Conference (CCDC) (2019)
Zhang, S., Wang, X., Wang, Z.: Weed recognition in wheat field based on sparse representation classification. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2019. LNCS, vol. 11643, pp. 511–519. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26763-6_49
García-Murillo, D.G., Álvarez, A.M., Cárdenas-Peña, D., Hincapie-Restrepo, W., Castellanos-Dominguez, G.: Sparse-based feature selection for discriminating between crops and weeds using field images. In: Nyström, I., Hernández Heredia, Y., Milián Núñez, V. (eds.) CIARP 2019. LNCS, vol. 11896, pp. 357–364. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33904-3_33
Espejo-Garcia, B., et al.: Towards weeds identification assistance through transfer learning. Comput. Electron. Agric. 171, 105306 (2020)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tensorflow. https://www.tensorflow.org/
ImageNet. https://image-net.org/
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, L., Oczkowski, A., Soomro, T.A., Wu, H. (2023). Rapid On-Site Weed Identification with Machine Learning. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-26431-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26430-6
Online ISBN: 978-3-031-26431-3
eBook Packages: Computer ScienceComputer Science (R0)