Plant Protect. Sci., 2023, 59(3):292-297 | DOI: 10.17221/131/2022-PPS

Verification of a machine learning model for weed detection in maize (Zea mays) using infrared imagingOriginal Paper

Adam Hruška1, Pavel Hamouz1
1 Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic

The potential of the framework of precision agriculture points towards the emergence of site-specific weed control. In light of the phenomena, the search for a cost-effective approach can help the discipline to accelerate the practical implementation. The paper presents a near-infrared data-driven machine learning model for real-time weed detection in wide-row cultivated maize (Zea mays) fields. The basis of the model is a dataset of 5 120 objects including 18 species of weeds significant in the context of wide-row crop production in the Czech Republic. The custom model was subsequently compared with a state-of-the-art machine learning tool You only look once (version 3). The custom model achieved 94.5 % identification accuracy while highlighting the practical limitations of the dataset.

Keywords: computer vision; NIR images; machine learning; visual analysis; neural networks

Accepted: June 6, 2023; Prepublished online: August 2, 2023; Published: September 20, 2023  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Hruška A, Hamouz P. Verification of a machine learning model for weed detection in maize (Zea mays) using infrared imaging. Plant Protect. Sci. 2023;59(3):292-297. doi: 10.17221/131/2022-PPS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Alam M., Alam M.S., Roman M., Tufail M., Khan M.U., Khan M.T. (2020): Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. In: 7th International Conference on Electrical and Electronics Engineering, April 14-16, 2020, Antalya, Turkey: 273-280. Go to original source...
    2. Dyrmann M., Karstoft H., Midtiby H.S. (2016): Plant species classification using deep convolutional neural network. Biosystems Engineering, 151: 72-80. Go to original source...
    3. Farooq A., Jia X., Hu J., Zhou J. (2019): Multi-resolution weed classification via convolutional neural network and superpixel based local binary pattern using remote sensing images. Remote Sensing, 11: 1692. doi: 10.3390/rs11141692 Go to original source...
    4. Fawakherji M., Potena C., Pretto A., Bloisi D.D., Nardi D. (2021): Multi-spectral image synthesis for crop/weed segmentation in precision farming. Robotics and Autonomous Systems, 146: 103861. doi: 10.1016/j.robot.2021.103861 Go to original source...
    5. Gebbers R., Adamchuk V.I. (2010): Precision agriculture and food security. Science, 327: 828-831. Go to original source... Go to PubMed...
    6. Gerhards R., Sökefeld M., Nabout A., Therburg R.D., Kühbauch W. (2002): Online weed control using digital image analysis. Journal of plant diseasesand protection,
    7. 18: 421-427.
    8. Ioffe S., Szegedy C. (2015): Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. PMLR,
    9. 37: 448-456.
    10. Junior L.C.M., Ulson J.A.C. (2021): Real time weed detection using computer vision and deep learning. In: 2021 14th IEEE International Conference on Industry Applications, September 10, 2021, Sao Paulo, Brasil: 1131-1137). Go to original source...
    11. Kaplan Z., Danihelka J., Chrtek J., Kirschner J., Kubát K., Štech M., Štěpánek J. (Eds.), 2019. Klíč ke květeně České republiky [Key to the flora of the Czech Republic], Academia, Praha.
    12. Liakos K.G., Busato P., Moshou D., Pearson S., Bochtis D. (2018): Machine learning in agriculture: A review. Sensors, 18: 2674. doi: 10.3390/s18082674 Go to original source... Go to PubMed...
    13. López-Granados F. (2011): Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research, 51: 1-11. Go to original source...
    14. Miikkulainen R., Liang J., Meyerson E., Rawal A., Fink D., Francon O., Raju B, Shahrzad H., Navruzyan A., Duffy N., Hodjat B. (2019): Evolving deep neural networks. Artificial intelligence in the age of neural networks and brain computing. Elsevier Academic Press: 293-312. Go to original source...
    15. Nawara J. (2010): Machine learning: face recognition technology evidence in criminal trials. University of Louisville Law Review, 49: 601.
    16. O'Mahony N., Campbell S., Carvalho A., Harapanahalli S., Hernandez G.V., Krpalkova L., Riordan D., Walsh J. (2020): Deep learning vs. traditional computer vision. In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference, April 25-26, 2019, Las Vegas, Go to original source...
    17. USA: 128-144.
    18. Potena C., Nardi D., Pretto A. (2017): Fast and accurate crop and weed identification with summarized train sets for precision agriculture. In: International Conference on Intelligent Autonomous Systems. Springer, Cham: 105-121. Go to original source...
    19. Pretty J. (2008): Agricultural sustainability: concepts, principles and evidence. Philosophical Transactions of the Royal Society B: Biological Sciences, 363: 447-465. Go to original source... Go to PubMed...
    20. Redmon J., Farhadi A. (2018). Yolov3: An incremental improvement. ArXiv preprint. arXiv:1804.02767
    21. Wang K., Gao X., Zhao Y., Li X., Dou D., Xu C.Z. (2019): Pay attention to features, transfer learn faster CNNs. International conference on learning representations. doi: 10.48550/arXiv.1804.02767 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content