AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning

• URL: http://arxiv.org/abs/2409.00735v1
• Date: Sun, 1 Sep 2024 14:55:45 GMT
• Title: AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
• Authors: 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,
• Abstract summary: We present AgGym, a modular, crop and stress simulation framework to model the spread of biotic stresses in a field. We show that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. Our proposed framework enables personalized decision support that can transform biotic stress management from being schedule based to opportunistic and prescriptive.
• Score: 8.205412609306713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agricultural production requires careful management of inputs such as fungicides, insecticides, and herbicides to ensure a successful crop that is high-yielding, profitable, and of superior seed quality. Current state-of-the-art field crop management relies on coarse-scale crop management strategies, where entire fields are sprayed with pest and disease-controlling chemicals, leading to increased cost and sub-optimal soil and crop management. To overcome these challenges and optimize crop production, we utilize machine learning tools within a virtual field environment to generate localized management plans for farmers to manage biotic threats while maximizing profits. Specifically, we present AgGym, a modular, crop and stress agnostic simulation framework to model the spread of biotic stresses in a field and estimate yield losses with and without chemical treatments. Our validation with real data shows that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. We further demonstrate that deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies with potential to increase yield recovery with less chemicals and lower cost. Our proposed framework enables personalized decision support that can transform biotic stress management from being schedule based and reactive to opportunistic and prescriptive. We also release the AgGym software implementation as a community resource and invite experts to contribute to this open-sourced publicly available modular environment framework. The source code can be accessed at: https://github.com/SCSLabISU/AgGym.

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