EGU25-14673, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-14673
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Friday, 02 May, 14:00–15:45 (CEST), Display time Friday, 02 May, 08:30–18:00
 
vPoster spot A, vPA.16
Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning Algorithms
Krishna Panthi1, Vidya Samadi2,3, and Carlos Toxtli1
Krishna Panthi et al.
  • 1School of Computing, Clemson University, Clemson, SC, USA
  • 2Agricultural Sciences Department, Clemson University, Clemson, SC, USA
  • 3Artificial Intelligence Research Institute for Science and Engineering, Clemson University, Clemson, SC, USA

Cotton is a one of the major crops in the southeastern United States. It significantly impacts regional water resources since it consumes a large amount of freshwater for irrigation. Current irrigation practices fail to optimize water use accurately since they are largely dependent on soil moisture sensors and grower experience. They do not consider dynamic factors such as soil texture, prevailing weather conditions, and the crop's phenological stage. In this paper we propose an innovative approach to enhance the irrigation efficiency through the use of Deep Reinforcement Learning (DRL) model. It takes into consideration the dynamic variables and optimizes irrigation. We utilize a crop growth simulation model as a learning environment to devise an optimal irrigation strategy. By continuously learning from crop feedback and environmental inputs, the DRL system dynamically modifies irrigation amount to optimize production while consuming the least amount of water. Our approach presents a viable alternative for sustainable irrigation decisions in water-intensive crops, since preliminary findings indicate that it can greatly conserve water without sacrificing crop health or productivity. The goal of this research is to aid in the advancement of precision irrigation technologies that guarantee cotton production's sustainability and resource efficiency. 

How to cite: Panthi, K., Samadi, V., and Toxtli, C.: Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning Algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14673, https://doi.org/10.5194/egusphere-egu25-14673, 2025.