Bayesian Learning for Cost Constrained Cultivation Management

There is a lot of previous research on profit maximization in agriculture. In previous research on cultivation management, the expected profit is maximized under the condition that probabilities are unknown. There is no income until the harvest season in agriculture. In reality, there is a limit to...

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Bibliographic Details
Published inJournal of Biomedical Fuzzy Systems Association Vol. 25; no. 1; pp. 21 - 31
Main Author MAEDA, Yasunari
Format Journal Article
LanguageJapanese
Published Biomedical Fuzzy Systems Association 10.06.2023
Subjects
cost constraint
cultivation management
dynamic programming
Markov decision process
unknown probability
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Summary:There is a lot of previous research on profit maximization in agriculture. In previous research on cultivation management, the expected profit is maximized under the condition that probabilities are unknown. There is no income until the harvest season in agriculture. In reality, there is a limit to the budget that can be spent on cultivation management. However, cost constraint has not been considered in the previous research. In this research, a new cost constrained cultivation management method is proposed. Cost constrained cultivation management is modeled by Markov decision processes with unknown probabilities. The proposed method maximizes the expected profit with respect to a Bayes criterion by dynamic programming. The effectiveness of the proposed method is shown by some numerical calculation examples. The expected profits according to the cost constraints are confirmed. The proposed method is effective for areas without historical data and areas affected by global warming. This research is a basic research, and future extended research is required.
ISSN:1345-1537
2424-2578
DOI:10.24466/jbfsa.25.1_21