Summary of Crops: a Deployable Crop Management System Over All Possible State Availabilities, by Jing Wu et al.
CROPS: A Deployable Crop Management System Over All Possible State Availabilities
by Jing Wu, Zhixin Lai, Shengjie Liu, Suiyao Chen, Ran Tao, Pan Zhao, Chuyuan Tao, Yikun Cheng, Naira Hovakimyan
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a deployable crop management system called CROPS that optimizes nitrogen and irrigation strategies to improve crop yield, economic profit, and environmental sustainability. CROPS employs a language model-based reinforcement learning agent to explore optimal management strategies within the DSSAT crop simulations. The system’s distinguishing feature is its use of partially observed states, which enhances the RL agent’s robustness and adaptability across various agricultural scenarios. Experiments on maize crops in Florida and Spain validate the effectiveness of CROPS, achieving state-of-the-art results in terms of production, profit, and sustainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a smart system called CROPS to help farmers grow more food using less water and fertilizer. The system uses a special kind of artificial intelligence (AI) that learns from data to find the best ways to take care of crops. This AI is very good at adapting to different situations, like weather or soil types. Scientists tested this system on corn farms in two places and found it worked really well, producing more food while being better for the environment. |
Keywords
» Artificial intelligence » Language model » Reinforcement learning
