Open Access
| Issue |
BIO Web Conf.
Volume 125, 2024
The 10th International Conference on Agricultural and Biological Sciences (ABS 2024)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 13 | |
| Section | Sustainable Agriculture, Soil and Plant Science | |
| DOI | https://doi.org/10.1051/bioconf/202412501004 | |
| Published online | 23 August 2024 | |
- T. Gergely, N. Gombkötő, N. Anikó, Drone agricultural area measurement using color filtering and fuzzy logic, Acta Agron. Óvár. 60, pp. 118–138, 2019. [Google Scholar]
- A. S. M. M. Hasan, D. Diepeveen, H. Laga, M. G. K. Jones, F. Sohel, Object-level benchmark for deep learning-based detection and classification of weed species, Crop Prot. 177, p. 106561, Mar. 2024, doi: 10.1016/j.cropro.2023.106561. [CrossRef] [Google Scholar]
- Y. Dandekar, K. Shinde, J. Gangan, S. Firdausi, S. Bharne, Weed plant detection from agricultural field images using YOLOv3 algorithm, in 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA, Pune, India: IEEE, Aug. 2022, pp. 1–4. doi: 10.1109/ICCUBEA54992.2022.10011010. [Google Scholar]
- J. P. Ramirez-Paredes, U.-H. Hernandez-Belmonte, Visual quality assessment of malting barley using color, shape and texture descriptors, Comp. Electr. Agricul., 168, p. 105110, Jan. 2020, doi: 10.1016/j.compag.2019.105110. [CrossRef] [Google Scholar]
- A. K. Bhunia, A. Bhattacharyya, P. Banerjee, P. P. Roy, S. Murala, A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern, Pattern Anal Applic, 23, no. 2, pp. 703–723, May 2020, doi: 10.1007/s10044-019-00827-x. [Google Scholar]
- E. Hamuda, B. Mc Ginley, M. Glavin, E. Jones, Automatic crop detection under field conditions using the HSV colour space and morphological operations, Comp. Electr. Agricul., vol. 133, pp. 97–107, Feb. 2017, doi: 10.1016/j.compag.2016.11.021. [CrossRef] [Google Scholar]
- L. Zhang, Z. Zhang, C. Wu, L. Sun, Segmentation algorithm for overlap recognition of seedling lettuce and weeds based on SVM and image blocking, Comp. Electr. Agricul. vol. 201, p. 107284, Oct. 2022, doi: 10.1016/j.compag.2022.107284. [CrossRef] [Google Scholar]
- A. Bakhshipour, A. Jafari, Evaluation of support vector machine and artificial neural networks in weed detection using shape features, Comp. Electr. Agricul. 145, pp. 153–160, Feb. 2018, doi: 10.1016/j.compag.2017.12.032. [CrossRef] [Google Scholar]
- H. Zhu et al., Research on improved YOLOx weed detection based on lightweight attention module, Crop Prot., 177, p. 106563, Mar. 2024, doi: 10.1016/j.cropro.2023.106563. [CrossRef] [Google Scholar]
- J. Ahmad et al., Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems, Comp. Ind., vol. 98, pp. 23–33, Jun. 2018, doi: 10.1016/j.compind.2018.02.005. [CrossRef] [Google Scholar]
- S. Abouzahir, M. Sadik, E. Sabir, Bag-of-visual-words-augmented histogram of oriented gradients for efficient weed detection, Biosyst. Engin., 202, pp. 179–194, Feb. 2021, doi: 10.1016/j.biosystemseng.2020.11.005. [CrossRef] [Google Scholar]
- S. G C, Y. Zhang, C. Koparan, M. R. Ahmed, K. Howatt, X. Sun, Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions, J. Agricul. Food Res., 9, p. 100325, Sep. 2022, doi: 10.1016/j.jafr.2022.100325. [CrossRef] [Google Scholar]
- M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, F. López-Granados, A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method, Appl. Soft Comp., 37, pp. 533–544, Dec. 2015, doi: 10.1016/j.asoc.2015.08.027. [CrossRef] [Google Scholar]
- V. Nguyen Thanh Le, B. Apopei, and K. Alameh, ‘Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods’, Information Processing in Agriculture, vol. 6, no. 1, pp. 116–131, Mar. 2019, doi: 10.1016/j.inpa.2018.08.002. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.