Yukimasa Kaneda

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Agricultural systems using advanced information and communication (ICT) technology can produce high-quality crops in a stable environment while decreasing the need for manual labor. The system collects a wide variety of environmental data and provides the precise cultivation control needed to produce high value-added crops; however, there are the problems(More)
This paper aims to reveal the appropriate amount of training data for accurately and quickly building a support vector regression (SVR) model for micrometeorological data prediction. SVR is derived from statistical learning theory and can be used to predict a quantity in the future based on training that uses past data. Although SVR is superior to(More)
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