Management of agriculture through artificial intelligence in adverse climatic conditions

Author:

Gupta Sheetanshu,Singh Nirbhan,Kashyap Shakuli

Abstract

Climate change has been a significant global challenge in recent years, resulting in adverse conditions for agricultural crops. Adverse climatic conditions, such as drought, flood, and extreme temperatures, have a significant impact on crop yields, resulting in food insecurity, economic losses, and environmental degradation. Agricultural experts have been working to develop innovative technologies to help farmers manage their crops better in adverse climatic conditions. One such technology is the use of Artificial Intelligence (AI) to model and manage agricultural crops. The main concern of this paper is to find the various applications of Artificial intelligence in agriculture to optimize irrigation and fertilizer application in adverse climatic conditions. By analyzing data on soil moisture levels and weather patterns, AI algorithms can determine the optimal timing and amount of irrigation and fertilizer application to maximize crop yield while minimizing water usage and fertilizer runoff. AI-based modeling and management of agricultural crops in adverse climatic conditions can help farmers improve crop yields, reduce costs, and mitigate the effects of climate change.

Publisher

Action For Sustainable Efficacious Development and Awareness

Subject

General Medicine

Reference12 articles.

1. Ahirwar, S., Swarnkar, R., Bhukya, S., Namwade, G., 2019. Application of drones in agriculture. Int. J. Curr. Microbiol. App. Sci. 8 (1), 2500–2505.

2. Arvind, G., Athira, V.G., Haripriya, H., Rani, R.A., Aravind, S., 2017. Automated irrigation with advanced seed germination and pest control. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR).

3. Bochtis, D. D., Sørensen, C. G., Green, O., Zalidis, G. C., &Kittas, C. (2019). Big data in agriculture: A challenge for the future. Agriculture, 9(10), 215.

4. Choudhary, S., Gaurav, V., Singh, A., Agarwal, S., 2019. Autonomous crop irrigation system using artificial intelligence. International Journal of Engineering and Advanced Technology. 8 (5S), 46–51.

5. Hemalatha, T., Sujatha, B., 2015. Sensor based autonomous field monitoring agriculture robot providing data acquisition & wireless transmission. International Journal of Innovative Research in Computer and Communication Engineering. 3 (8), 7651–7657.

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