Cost Constrained Cultivation Management With Sensors Considering Fluctuation of Crop Price

There is a lot of previous research on cultivation management based on mathematical engineering. In some previous research, the expected profit of cost-constrained cultivation management for multiple agricultural fields with sensors is maximized under the condition that growing states are unknown. However, in the previous research, it is assumed that the same crop will be grown in all agricultural fields and crop prices do not fluctuate. In this research, cost-constrained cultivation management with sensors considering fluctuation of crop price under the condition that different crops can be grown in the agricultural fields and growing states are unknown is studied. The cultivation management problem is modeled by Markov decision processes, as in previous research. The expected profit is maximized with reference to a Bayes criterion using dynamic programming in the proposed method. The effectiveness of the proposed method is shown by some computational examples. Adaptive cost adjustment between the different crops is confirmed in the computational examples.