Summary of Aggym: An Agricultural Biotic Stress Simulation Environment For Ultra-precision Management Planning, by Mahsa Khosravi et al.
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
by Mahsa Khosravi, Matthew Carroll, Kai Liang Tan, Liza Van der Laan, Joscif Raigne, Daren S. Mueller, Arti Singh, Aditya Balu, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A machine learning framework, AgGym, is proposed to optimize crop production by generating localized management plans for farmers. The framework uses a virtual field environment to simulate the spread of biotic stresses and estimate yield losses with and without chemical treatments. Validation with real data shows that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. Additionally, deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies that increase yield recovery with less chemicals and lower cost. The framework enables personalized decision support, transforming biotic stress management from being schedule-based and reactive to opportunistic and prescriptive. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Agricultural production requires careful management of inputs like fungicides and insecticides to ensure a successful crop. Current methods rely on coarse-scale crop management strategies that can be costly and suboptimal. To optimize crop production, a machine learning framework called AgGym is proposed. It uses a virtual field environment to simulate the spread of biotic stresses and estimate yield losses with and without chemical treatments. The framework shows promise in increasing yield recovery while reducing chemicals and cost. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
