Robust Pest Management Using Reinforcement Learning

Developing effective decision support systems for agriculture matters. Human population is likely to peak at close 11 billion and changing climate is already reducing yields in the Great Plains and in other fertile regions across the world. With virtually all arable land already cultivated, the only way to feed the growing human population is to increase yields. Making better decisions, driven by data, can increase the yield and quality of agricultural products and reduce their environmental impact. In this work, we address the problem of an apple orchardist who must decide how to control the population of codling moth, which is an important apple pest. The orchadist must decide when to apply pesticides to optimally trade off apple yields and quality with the financial and environmental costs of using pesticides. Pesticide spraying decisions are made weekly throughout the growing season, with the yield only observed at the end of the growing season. The inherent stochasticity driven by weather and delayed rewards make this a classical reinforcement learning problem. Deploying decision support systems in agriculture is challenging. Farmers are averse to risk and do not trust purely data-driven recommendations. Because weather varies from season to season and ecological systems are complex even a decade worth of data may be insufficient to get good decisions with high confidence. We propose a robust reinforcement learning approach that can compute good solutions even when the models or rewards are not known precisely. We use Bayesian models to capture prior knowledge. Our main contribution is that we evaluate which model and reward uncertainties have the greatest impact on solution quality.

Authors

• Talha Siddique
• Jia Lin Hau
• Marek Petrik University of New Hampshire, Durham
• Shadi Atallah

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