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Optimal Crop Fertilization Control Strategies and Verification
This project deals with the design and analysis of fertilization control strategies. The goal is to minimize over-fertilization while ensuring sufficient nutrification of the crops. Therefore, it is required to study literature on dynamical models of nitrogen in soil, extract a suitable model and implement it in a simulation. Then, design a suitable, formally verifyable control algorithm and analyse the potential of optimal fertilization strategies in agriculture. The control tools may range from dynamic programming (with a-priori guarantees) to reinforcement learning (with statistical a-posteriori guarantees) and beyond.
Nitrogen fertilizer has been critical to supporting high crop yields; however,
nitrogen is also susceptible to being washed from agricultural fields into water
bodies, where it degrades water quality. The aim of this work is to characterize
the risk of a crop nitrogen deficit under different farmer decisions and to identify
the potential for reducing fertilizer application without jeoparding crop yields.
**The main questions are**
**1.** How do we model the dynamics of the nitrogen concentration in the soil?
**2.** The evolution of the nitrogen concentration is uncertain due to disturbances (rain), unknown model parameters and imprecise measurements. How can we quantify the risk of crossing a given nitrogen deficit threshold in a meaningful way?
**3.** How do we compute an optimal fertilization strategy to minimize the required fertilizer while satisfying the given risk level?
**4.** How much (cumulative) fertilizer could be conserved by optimal fertilization? How does shortening the time between measuring soil levels and/or re-applying fertilizer effect the control performance?
**References**
[1] Paolo D’Odorico et al. “Probabilistic modeling of nitrogen and carbon dynamics in water-limited ecosystems”. In: Ecological Modelling 179.2 (2004), pp. 205–219.
[2] Dong Kook Woo and Praveen Kumar. MLCan 2.0. url: https://github.com/HydroComplexity/MLCan2.0.
[3] Niklas Schmid et al. “Computing optimal joint chance constrained control policies”. In: arXiv preprint arXiv:2312.10495 (2023).
[4] SWAT. SWAT+. url: https://swat.tamu.edu/.
Nitrogen fertilizer has been critical to supporting high crop yields; however, nitrogen is also susceptible to being washed from agricultural fields into water bodies, where it degrades water quality. The aim of this work is to characterize the risk of a crop nitrogen deficit under different farmer decisions and to identify the potential for reducing fertilizer application without jeoparding crop yields.
**The main questions are**
**1.** How do we model the dynamics of the nitrogen concentration in the soil?
**2.** The evolution of the nitrogen concentration is uncertain due to disturbances (rain), unknown model parameters and imprecise measurements. How can we quantify the risk of crossing a given nitrogen deficit threshold in a meaningful way?
**3.** How do we compute an optimal fertilization strategy to minimize the required fertilizer while satisfying the given risk level?
**4.** How much (cumulative) fertilizer could be conserved by optimal fertilization? How does shortening the time between measuring soil levels and/or re-applying fertilizer effect the control performance?
**References**
[1] Paolo D’Odorico et al. “Probabilistic modeling of nitrogen and carbon dynamics in water-limited ecosystems”. In: Ecological Modelling 179.2 (2004), pp. 205–219.
[3] Niklas Schmid et al. “Computing optimal joint chance constrained control policies”. In: arXiv preprint arXiv:2312.10495 (2023).
[4] SWAT. SWAT+. url: https://swat.tamu.edu/.
**1.** Study literature on dynamical models of nitrogen [1, 2, 4].
**2.** Implement a simulation based on a realistic model.
**3.** Study literature on verification and suitable control tools [3].
**4.** Formulate a suitable risk-constrained optimization problem and solve it using a respective control algorithm.
**5.** Analyse the aforementioned questions using the designed simulation and controller.
**1.** Study literature on dynamical models of nitrogen [1, 2, 4].
**2.** Implement a simulation based on a realistic model.
**3.** Study literature on verification and suitable control tools [3].
**4.** Formulate a suitable risk-constrained optimization problem and solve it using a respective control algorithm.
**5.** Analyse the aforementioned questions using the designed simulation and controller.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records via email to nikschmid@ethz.ch, kwallington@ethz.ch.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records via email to nikschmid@ethz.ch, kwallington@ethz.ch.
