可視・近赤外スペクトルを用いた土壌成分値予測モデルの構築と評価 The development and validation of the prediction models for soil properties using VIS/NIR spectra.

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Abstract

精密農業においては、可変施肥や散水の効率化のため、ほ場の状態を正確にかつ迅速に把握することが求められている。そこで我々は、土中光センサーを用いて測定した土壌の可視・近赤外スペクトルを利用して、土壌に含まれる水分量や炭素量、窒素量などの土壌成分値を推定するための研究を進めている。本発表では、回帰モデルを構築し、その予測性や非線型性について評価した結果を発表する。

In recent years, precision agriculture has been widely applied to farming practices in order to improve their productivity, and to reduce their environmental impacts. One of the important challenges in the precision agriculture is to determine the soil properties of an agricultural field accurately, and yet rapidly. Such evaluation is essential to executing well-timed, appropriate actions, which could improve farming efficiency. To this end, we have developed statistical models that are capable of predicting soil properties of a field, such as moisture, nitrogen, or carbon contents. These models are constructed from visible/near-infrared spectra data obtained by a real-time soil spectrometer. In this presentation we discuss a few approaches and methods to improve the predictive power of such models. Support vector regression (SVR) method allows us to obtain reasonably predictive models without explicit variable selection or wavelength selection models. Ensemble learning procedure could significantly improve the predictivity of partial least square (PLS) models. Furthermore the applicability domain of a model can be estimated through application of bootstrap technique in calculation of standard deviation of each predicted values.

Journal

  • Proceedings of the Symposium on Chemoinformatics

    Proceedings of the Symposium on Chemoinformatics 2010(0), J07-J07, 2010

    Division of Chemical Information and Computer Sciences The Chemical Society of Japan

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