Abstract

This paper presents a novel approach in tackling the problem by introducing machine learning technique, specifically, Reinforcement Learning (RL) and use it to search for optimal solution. An example is given in the results section to demonstrate the effectiveness of RL. We also compare performance of the Reinforcement Learning with respect to a standard planning optimization technique, Dynamic Planning. Furthermore, implications of extending this general learning framework to solve various other agricultural problems are also discussed in this paper.

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