Open Access
| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01057 | |
| Number of page(s) | 13 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101057 | |
| Published online | 05 June 2023 | |
- U. M. P. Priya, “Predicting yield of the crop using machine learning Algorithm,” IJESRT, pp. 2277–2284, April (2018). [Google Scholar]
- R. K. M. S. a. P. W. Georg Ruß, “Estimation of neural network parameters for wheat yield prediction,” In Max Bramer, editor, Artificial Intelligence in Theory and Practice II, volume 276 of IFIP International Federation for Information Processing, p. 109–118, July (2008). [Google Scholar]
- S. Myers, M. Smith, S. Guth, C. Golden, B. Vaitla, N. Mueller, A. Dangour and Huybers, “P. Climate change and global food systems: potential impacts on food security and undernutrition.,” Annu. Rev. Public Health, pp. 259–277, (2017). [CrossRef] [PubMed] [Google Scholar]
- V. G. Ajinkya Paikekari, “Weed detection using image processing,” International Research Journal of Engineering and Technology. [Google Scholar]
- R. Desai, “Removal of weeds using Image Processing,” International Journal of Advanced Computer Technology (IJACT), pp. 1–5, (2016). [Google Scholar]
- A. K. Shinde, “Crop detection by machine vision for weed management,” International Journal of Advances in Engineering & Technology., (2019). [Google Scholar]
- M. R. et al., “Weed Detection in Soybean Fields Using Deep Learning and Image Processing Techniques,” Computers and Electronics in Agriculture, pp. 70–80, (2018). [Google Scholar]
- N. K. Singh, “A Review on Weed Detection Techniques in Agriculture,” Springer Link, pp. 1611–1632, (2020). [Google Scholar]
- S. V. T. et al., “Crop Fertilizer Prediction Using Machine Learning Techniques: A Comprehensive Review,” ResearchGate, (2021). [Google Scholar]
- Y. Dong, “Crop Nutrient Prediction Using Machine Learning Techniques and Soil Data,” European Journal Of Agronomy, (2021). [PubMed] [Google Scholar]
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