SUPPLY CHAIN OPTIMIZATION IN AGRICULTURE USING ARTIFICIAL INTELLIGENCE

Authors

  • Saumya Rajvanshi Department of Computer Science, Punjabi University, Patiala, Punjab, India.
  • Gurleen Kaur Chandigarh Group of Colleges, Landran, Mohali, Punjab, India
  • Gaurav Deep Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, India.

Keywords:

Supply Chain Optimization, Artificial Intelligence, IoT

Abstract

Agriculture is vital to nation’s economy as it fulfills the food demand of increasing population .The demand for sustainable practices increases and supply chain optimization using Artificial Intelligence (AI) is the most promising technology in agriculture sector. This article reviews the recent research done in the field of supply chain based on various AI techniques. The process of supply chain in agriculture is discussed and various AI tools are discussed which helps in mitigating the problems of supply chain. The research gaps in the current scenario are also discussed.

 

References

Bai, S., Wang, Y., Zheng, S., & He, H. (2023). Green Investment Decisions and Coordination in a Green Agri-Product Supply Chain considering Risk Aversion and Bargaining Power under Different Channel Power Structures. Complexity, 2023. https://doi.org/10.1155/2023/6401962

Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001

Mishra, A. C., Das, J., & Awtar, R. (n.d.). An Emerging Era Of Research In Agriculture Using AI. www.jsrtjournal.com

Olabimpe Banke Akintuyi. (2024). Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Open Access Research Journal of Multidisciplinary Studies, 7(2), 016–030. https://doi.org/10.53022/oarjms.2024.7.2.0023

Oluwafunmi Adijat Elufioye, Chinedu Ugochukwu Ike, Olubusola Odeyemi, Favour Oluwadamilare Usman, & Noluthando Zamanjomane Mhlongo. (2024). AI-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing The Benefits And Challenges Of AI In Forecasting Demand And Optimizing Supply In Agriculture.Computer Science & IT Research Journal, 5(2), 473–497. https://doi.org/10.51594/csitrj.v5i2.817

Rahim, M. K. I. A., Harahap, A. Z. M. K., Bolaji, B. H., & Ahmad, A. N. A. (2023a). Optimizing a Multi Period Deterministic Inventory Routing Problem in Agriculture Industries. Paper Asia, 39(5), 40–47. https://doi.org/10.59953/comp.by.paperasia.v39i5(b).35

Rahim, M. K. I. A., Harahap, A. Z. M. K., Bolaji, B. H., & Ahmad, A. N. A. (2023b). Optimizing a Multi Period Deterministic Inventory Routing Problem in Agriculture Industries. Paper Asia, 39(5), 40–47. https://doi.org/10.59953/comp.by.paperasia.v39i5(b).35

Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F. J., Castagnini, J. M., Leksawasdi, N., & Phimolsiripol, Y. (2023). Artificial Intelligence: Implications for the Agri-Food Sector. In Agronomy (Vol. 13, Issue 5). MDPI. https://doi.org/10.3390/agronomy13051397

Vatin, N. I., John, V., Nangia, R., Kumar, M., & Prasanna, Y. L. (2024). Supply Chain Optimization in Industry 5.0: An Experimental Investigation Using Al. BIO Web of Conferences, 86. https://doi.org/10.1051/bioconf/20248601093

Downloads

Published

2024-12-29

How to Cite

Saumya Rajvanshi, Gurleen Kaur, & Gaurav Deep. (2024). SUPPLY CHAIN OPTIMIZATION IN AGRICULTURE USING ARTIFICIAL INTELLIGENCE. Journal Punjab Academy of Sciences, 24, 58–62. Retrieved from https://jpas.in/index.php/home/article/view/101