A method for estimating productivity of Zoysia japonica using neural network
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概要
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Two production models, one using a neural network and the other using a multiple regression analysis, were constructed based on data of monthly dry matter production under various climatic conditions for <I>Zoysia japonica</I>. These models were then evaluated for practical use by comparing conformity of estimated data with actual harvest data. Using 105 items of harvest data with <I>Zoysia japonica</I> for three years, these models were compared for the estimation accuracy each other. The results clearly showed that the neural network had a higher coefficient of correlation between estimated data and harvest one. Accordingly, the neural network model showed the better estimation for the productivity.
- 日本芝草学会の論文
日本芝草学会 | 論文
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