ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PRECISION FARMING



EOI: 10.11242/viva-tech.01.06.013

Download Full Text here



Citation

Prof. Chandani Patel ,Soham Waglekar, Atharva Yadav, "ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PRECISION FARMING", VIVA-IJRI Volume 1, Issue 6, Article 1, pp. 1-8, 2023. Published by mca Department, VIVA Institute of Technology, Virar, India.

Abstract

Rapid socioeconomic change is opening up new areas of application for precision agriculture in certain developing nations, notably India. The high-tech aspect of traditional PA technologies for emerging countries has enormous consequences for economic development, urbanisation, and energy consumption in some developing countries. The authors' investigation into the various uses of the most recent information technology in agriculture is presented in this study. This article offers details on how various receivers and pieces of software can be used and applied to benefit modern agriculture. Numerous opportunities are opened up by these technologies and their applications, such as resource mapping in nature and impact assessments of environmental changes.

Keywords

Adaption, Artificial Intelligence (AI), Development Machines, Geographic Systems, precision Agriculture, promising solutions.

References

  1. De Smith M., Goodchild M, Longley P. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Leicester: Matador; 2007.
  2. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
  3. Cao, Q., & Zhang, Y. (2020). A Review of Deep Learning Applications in Precision Agriculture. Journal of Imaging, 6(3), 25. https://doi.org/10.3390/jimaging6030025
  4. Chen, S., Huang, J., Liao, W., Chen, X., Zhang, H., Zou, H., & Zhang, Y. (2018). Application of artificial intelligence in precision agriculture: A review. Journal of Agricultural Science, 10(9), 297-310. https://doi.org/10.5539/jas.v10n9p297
  5. Kathleen Walch, Cognitive World: How AI is Transforming Agriculture: https://www.forbes.com/sites/cognitiveworld/2019/07/05/ how-ai-is-transforming agriculture/#5f0055694ad1
  6. Schroder, D., Haneklaus, S. and Schung, E. (1997) Information management in precision agriculture with LORIS. In Precision Agriculture’97, Vol.II: Technology, IT and Management (Ed. J.V. Stafford). BIOS Scientific Publishers Ltd., Oxford, UK. pp.821
  7. Geospatial World : Precision Farming in Indian Context https://www.geospatialworld.net/article/precision- farming-in-indian-context/
  8. Barnes, E.M., Moran, M.S., Pinter, P.J. Jr and Clark, T.R. 1996. Multispectral remote sensing and site-specific agriculture: examples of current technology and future possibilities. Published in Proc. of 3rd Int. Conf. on Precision Agriculture, June 23-26, 1996, Minneapolis, Minnesota, ASA. pp.843-854./
  9. Moran, M.S. , Inoue, Y. and Barnes, E.M. 1997. Opportunities and limitations for image –based remote sensing in precision crop management. Remote Sensing of Environment. 61: 319-346.
  10. http://www.autodesk.com/
  11. Shanwad UK, Patil Vc, Gowda HH. Precision Farming: dreams and realities for Indian agriculture. http://www.GISdevelopment.net
  12. Precision Farming :AI and Automation Are Transforming Agriculture: https://datacenterfrontier.com/precision-farming-ai- and-automation-are-transforming-agriculture/
  13. Panigrahi, S., Singh, D., & Singh, R. K. (2021). Machine learning and deep learning approaches for crop yield prediction: A review. Computers and Electronics in Agriculture, 184, 106001. https://doi.org/10.1016/j.compag.2020.106001
  14. Xie, S., Chen, Y., & Zhou, Z. (2020). A review of the application of artificial intelligence in smart agriculture. Journal of Physics: Conference Series, 1629, 012052. https://doi.org/10.1088/1742-6596/1629/1/012052
  15. Ren, J., & Liu, Y. (2020). A Review of Application of Deep Learning in Precision Agriculture. Journal of Physics: Conference Series, 1464, 012123. https://doi.org/10.1088/1742-6596/1464/1/012123