Open Access
Issue
E3S Web of Conf.
Volume 507, 2024
International Conference on Futuristic Trends in Engineering, Science & Technology (ICFTEST-2024)
Article Number 01066
Number of page(s) 14
DOI https://doi.org/10.1051/e3sconf/202450701066
Published online 29 March 2024
  1. Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique, Rakesh Kumar, M.P. Singh, Prabhat Kumar and J.P. Singh, 2015 International Conference on Smart Technologies and Management for Computing, Communication,Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&DInstitute of Science and Technology, Chennai, T.N., India. 6 -8 May 2015. pp.138-145. [Google Scholar]
  2. Prediction of crop growth using machine learning based on seed features, N. Nandhini and J. Gowri Shankar, Prediction of crop growth using machine learning based on seed features DOI: 10.21917/ijsc.2020.031 [Google Scholar]
  3. Crop Yield Analysis Using Machine Learning Algorithms, Fatin Farhan Haque, Ahmed Abdelgawad, Venkata Prasanth Yanambaka, Kumar Yelamarth, College of Engineering andTechnology Central Michigan University, ET100 Mount Pleasant, MI 48858, US [Google Scholar]
  4. A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms Saravana Kumar Venkatesan, Jonghyun Lim, and Yongyun Cho, Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 2648695 [Google Scholar]
  5. Crop Yield Prediction using Machine Learning Algorithm D. Jayanarayana Reddy,Dr M. Rudra Kuma, Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021) IEEE Xplore Part Number: CFP21K74-ART; ISBN: 978-0-7381-1327-2 [Google Scholar]
  6. Predicting Plant Growth and Development Using Time-Series Images, Chunying Wang ,Writing Pan , Xubin Song , Haixia Yu , Junke Zhu , Ping Liu , and Xiang Li. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China 2 State Key Laboratory of Crop Biology, College of Life Sciences, ShandongAgricultural University, Taian 271018, China. [Google Scholar]
  7. Janaki, K., Venkata Shiva Varun, N., Manohar, K., Mani Kumar, K., Suresh, K., & Renuka, M. (2023)2. Deep learning to predict plant growth and yield in greenhouse environment International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(3), 489-4901. [Google Scholar]
  8. van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 [Google Scholar]
  9. Alioune Badara Sarr, Benjamin Sultan, Predicting crop yields in Senegal using machine learning methods. Int J Climatol 42, 180-191 (2022). https://doi.org/10.1002/joc.7947 [Google Scholar]
  10. M K Dharani, R Thamilselvan, P Natesan, PCD Kalaivaani, S Santhoshkumar, Review on Crop Prediction Using Deep Learning Techniques. J. Phys.: Conf. Ser. 1767, 012026 (2021). https://doi.org/10.1088/1742-6596/1767/1/012026 [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.