Open Access
Issue
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
Article Number 01015
Number of page(s) 5
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601015
Published online 23 May 2025
  1. S.N.S. Al-Humairi, P. Manimaran, M.I. Abdullah, J. Daud, A Smart Automated Greenhouse: Soil Moisture, Temperature Monitoring and Automatic Water Supply System (Peaty, Loam and Silty). IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies, CSUDET, 111–115 (2019). https://doi.org/10.1109/CSUDET.2019.8906933 [Google Scholar]
  2. Z. Guo, C. Yang, W. Yang, G. Chen, Z. Jiang, B. Wang, J. Zhang, Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-highdefinition aerial images in the field. J. Exp. Bot. 73(19), 6575–6588 (2022). https://doi.org/10.1093/jxb/erac274 [CrossRef] [Google Scholar]
  3. E. David, S. Madec, P. Sadeghi-Tehran, H. Aasen, B. Zheng, S. Liu, S.C. Chapman, Global wheat head detection (GWHD) dataset: A large and diverse dataset of highresolution RGB labelled images to develop and benchmark wheat head detection methods. Plant Methods 16(1), 1–17 (2020). https://doi.org/10.34133/2020/3521852 [CrossRef] [Google Scholar]
  4. G.L. Hartman, E.D. West, T.K. Herman, Crops that feed the World [Google Scholar]
  5. Soybean— worldwide production, use, and constraints caused by pathogens and pests. Food Security 3(1), 5–17 (2011). https://doi.org/10.1007/s12571-010-0108-x [CrossRef] [Google Scholar]
  6. Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat. Eng. Sci. 8(3), 214–232 (2023). https://doi.org/10.28978/nesciences.1405175 [Google Scholar]
  7. A. Kamilaris, F.X. Prenafeta-Boldú, Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018). https://doi.org/10.1016/j.compag.2018.02.016 [CrossRef] [Google Scholar]
  8. K. Lin, L. Gong, Y. Huang, C. Liu, Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front. Plant Sci. 10, 155 (2019). https://doi.org/10.3389/fpls.2019.00155 [CrossRef] [Google Scholar]
  9. A. Radhika, M.S. Masood, Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique. J. Internet Serv. Inf. Secur. 12(4), 177–196 (2022). https://doi.org/10.22667/JISIS.2022.11.30.177 [Google Scholar]
  10. B. Liu, X. Cheng, Z. Zhang, Z. Yan, J. Li, Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci. Rep. 9(1), 1–10 (2019). https://doi.org/10.1038/s41598-019-42761-2 [Google Scholar]
  11. P.K. Paul, R.R. Sinha, P.S. Aithal, B. Aremu, R. Saavedra, Agricultural Informatics: An Overview of Integration of Agricultural Sciences and Information Science. Indian J. Inf. Sources Serv. 10(1), 48–55 (2020). https://doi.org/10.2139/ssrn.3764184 [Google Scholar]
  12. D.B. Lobell, K.G. Cassman, C.B. Field, Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179–204 (2009). https://doi.org/10.1146/annurev.environ.041008.093740 [CrossRef] [Google Scholar]
  13. A. Milioto, P. Lottes, C. Stachniss, Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. Rob. Auton. Syst. 113, 79–96 (2019). https://doi.org/10.1016/j.robot.2018.09.014 [Google Scholar]
  14. S.P. Mohanty, D.P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016). https://doi.org/10.3389/fpls.2016.01419 [CrossRef] [Google Scholar]
  15. M. Rahnemoonfar, C. Sheppard, Deep count: Fruit counting based on deep simulated learning. Sensors 17(4), 905 (2017). https://doi.org/10.3390/s17040905 [CrossRef] [Google Scholar]
  16. K. Veerasamy, E.J. Thomson Fredrik, Intelligence System towards Identify Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 14(4), 45–59 (2023). https://doi.org/10.22667/JOWUA.2023.12.31.045 [Google Scholar]
  17. I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez, C. McCool, DeepFruits: A fruit detection system using deep neural networks. Sensors 16(8), 1222 (2016). https://doi.org/10.3390/s16081222 [CrossRef] [PubMed] [Google Scholar]
  18. C. Shorten, T.M. Khoshgoftaar, A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https://doi.org/10.1186/s40537-019-0197-0 [CrossRef] [Google Scholar]
  19. T. Ngene, A. Mohamed, C. Didi, P. Oyekola, A geological assessment of natural resources in Umueje and environs. Int. J. Adv. Sci. Technol. 29(8) (2020). https://www.researchgate.net/publication/342548981 [Google Scholar]
  20. R. Xu, C. Li, A.H. Paterson, Y. Jiang, Multi-objective detection of sorghum heads based on deep learning networks. Plant Methods 16(1), 1–13 (2020). https://doi.org/10.1186/s13007-020-00565-w [CrossRef] [Google Scholar]
  21. G. Yang, J. Liu, C. Zhao, Z. Li, Y. Huang, H. Yu, D. Zhang, Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Front. Plant Sci. 11, 1148 (2020). https://doi.org/10.3389/fpls.2017.01111 [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.