EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 297 (2021) E3S Web Conf., 297 (2021) 01054 References
Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01054
Number of page(s) 8
DOI https://doi.org/10.1051/e3sconf/202129701054
Published online 22 September 2021
  1. R. Latif, A. Saddik and A. Elouardi, “Evaluation of Agricultural Precision Algorithms on UAV Images,” 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), 2019, pp. 1–4, doi: 10.1109/ICCSRE.2019.8807604. [Google Scholar]
  2. Sishodia, R.P.; Ray, R.L.; Singh, S.K. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens. 2020, 12, 3136. https://doi.org/10.3390/rs12193136. [Google Scholar]
  3. Kolady, D.E., Van der Sluis, E., Uddin, M.M. et al. Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota. Precision Agric 22, 689–710 (2021). https://doi.org/10.1007/s11119-020-09750-2. [Google Scholar]
  4. Zhang, Q., Chen, Q., Xu, Z. et al. Evaluating the navigation performance of multi-information integration based on low-end inertial sensors for precision agriculture. Precision Agric 22, 627–646 (2021). https://doi.org/10.1007/s11119-020-09747-x [Google Scholar]
  5. Afrasiabian, Y., Noory, H., Mokhtari, A. et al. Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran). Precision Agric 22, 660–688 (2021). https://doi.org/10.1007/s11119-020-09749-9 [Google Scholar]
  6. Kazama, E.H., da Silva, R.P., Tavares, T.O. et al. Methodology for selective coffee harvesting in management zones of yield and maturation. Precision Agric 22, 711–733 (2021). https://doi.org/10.1007/s11119-020-09751-1 [Google Scholar]
  7. Latif R., Jamad L., Saddik A. (2021) Implementation of Hybrid Algorithm for the UAV Images Preprocessing Based on Embedded Heterogeneous System: The Case of Precision Agriculture. In: Hassanien A.E., Darwish, A., Abd El-Kader S.M., Alboaneen, D.A. (eds) Enabling Machine Learning Applications in Data Science. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6129-4_11 [Google Scholar]
  8. Stefan K. Ericson, Björn S. Âstrand, Analysis of two visual odometry systems for use in an agricultural field environment, Biosystems Engineering, Volume 166, 2018, Pages 116–125, https://doi.org/10.1016/j.biosystemseng.2017.11.009. [Google Scholar]
  9. R. Latif and A. Saddik, “SLAM algorithms implementation in a UAV, based on a heterogeneous system: A survey,” 2019 4th World Conference on Complex Systems (WCCS), 2019, pp. 1–6, doi: 10.1109/IcoCS.2019.8930783. [Google Scholar]
  10. Rubio-Delgado, J., Pérez, C.J. & Vega-Rodriguez, M.A. Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture. Precision Agric 22, 1–21 (2021). https://doi.org/10.1007/s11119-020-09727-1 [Google Scholar]
  11. Lu, J., Li, W., Yu, M. et al. Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agric 22, 51–74 (2021). https://doi.org/10.1007/s11119-020-09729-z [Google Scholar]
  12. Rabab, S., Badenhorst, P., Chen, Y.P.P. et al. A template-free machine vision-based crop row detection algorithm. Precision Agric 22, 124–153 (2021). https://doi.org/10.1007/s11119-020-09732-4. [Google Scholar]
  13. R. Liu, M. Li, J.L. Guzman, F. Rodriguez, A fast and practical one-dimensional transient model for greenhouse temperature and humidity, Computers and Electronics in Agriculture, Volume 186, 2021, 106186, https://doi.org/10.1016/j.compag.2021.106186. [Google Scholar]
  14. Zichen Huang, Tomoo Shiigi, Lok Wai Jacky Tsay, Hiroaki Nakanishi, Tetsuhito Suzuki, Yuichi Ogawa, Kondo Naoshi, A sound-based positioning system with centimeter accuracy for mobile robots in a greenhouse using frequency shift compensation, Computers and Electronics in Agriculture, Volume 187, 2021, 106235, https://doi.org/10.1016/j.compag.2021.106235. [Google Scholar]
  15. Amine Saddik, Rachid Latif, Mohamed Elhoseny, Abdelhafid El Ouardi, Real-time evaluation of different indexes in precision agriculture using a heterogeneous embedded system, Sustainable Computing: Informatics and Systems, Volume 30, 2021, 100506, https://doi.org/10.1016/j.suscom.2020.100506. [Google Scholar]
  16. Konstantinos P. Ferentinos, Deep learning models for plant disease detection and diagnosis, Computers and Electronics in Agriculture, Volume 145, 2018, Pages 311–318, https://doi.org/10.1016/j.compag.2018.01.009. [Google Scholar]
  17. W. Yang, C. Yang, Z. Hao, C. Xie and M. Li, “Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network,” in IEEE Access, vol. 7, pp. 118239–118248, 2019, doi: 10.1109/ACCESS.2019.2936892. [Google Scholar]
  18. Yan Pang, Yeyin Shi, Shancheng Gao, Feng Jiang, Arun-Narenthiran Veeranampalayam-Sivakumar, Laura Thompson, Joe Luck, Chao Liu, Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery, Computers and Electronics in Agriculture, Volume 178, 2020, 105766, https://doi.org/10.1016/j.compag.2020.105766. [Google Scholar]
  19. Angela Casado-Garcia, Arantza del-Canto, Alvaro Sanz-Saez, Usue Pérez-Lopez, Amaia Bilbao-Kareaga, Felix B. Fritschi, Jon Miranda-Apodaca, Alberto Munoz-Rueda, Anna Sillero-Martinez, Ander Yoldi-Achalandabaso, Maite Lacuesta, Jonathan Heras, LabelStoma: A tool for stomata detection based on the YOLO algorithm, Computers and Electronics in Agriculture, Volume 178, 2020, 105751, https://doi.org/10.1016/j.compag.2020.105751. [Google Scholar]
  20. P.A. Dias, A. Tabb and H. Medeiros, “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network,” in IEEE Robotics and Automation Letters, vol. 3, no. 4 pp. 3003–3010, Oct. 2018, doi: 10.1109/LRA.2018.2849498. [Google Scholar]
  21. Xu Wang, Julie Tang, Mark Whitty, Side view apple flower mapping using edge-based fully convolutional networks for variable rate chemical thinning, Computers and Electronics in Agriculture, Volume 178, 2020, 105673, https://doi.org/10.1016Zj.compag.2020.105673. [Google Scholar]
  22. Y. Li et al., “Soybean Seed Counting Based on Pod Image Using Two-Column Convolution Neural Network,” in IEEE Access, vol. 7, pp. 64177–64185, 2019, doi: 10.1109/ACCESS.2019.2916931. [Google Scholar]
  23. Ahmed Al Makky, A. Alaswad, Desmond, Gibson, A.G. Olabi, Prediction of the gas emission from porous media with the concern of energy and environment, Renewable and Sustainable Energy Reviews, Volume 68, Part 2, 2017, Pages 1144–1156, https://doi.org/10.1016/j.rser.2016.08.001. [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.