Feb. 15, 2024, 5:41 a.m. | Zhaoan Wang, Shaoping Xiao, Jun Wang, Ashwin Parab, Shivam Patel

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08832v1 Announce Type: new
Abstract: This study examines how artificial intelligence (AI), especially Reinforcement Learning (RL), can be used in farming to boost crop yields, fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate change and limited agricultural knowledge, we use Partially Observable Markov Decision Processes (POMDPs) with a crop simulator to model AI agents' interactions with farming environments. We apply deep Q-learning with Recurrent Neural Network …

abstract artificial artificial intelligence arxiv boost change climate climate change cs.ai cs.cy cs.lg emissions farming greenhouse greenhouse gases intelligence intelligent management reduce reinforcement reinforcement learning study type

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