Development of Real-time Change Point Analysis for Field Environmental Information in Agriculture

Prehension and evaluation of field environmental conditions are very important tasks in agriculture. To enhance the agricultural production system, an agricultural information supporting system ”Agri-eye” has been developed based on a cloud computing scheme. It consists of work recording, actuation and field environmental monitoring system. Regarding the environmental observation, monitoring nodes have been installed in several greenhouses in Fukuoka, Japan up to now. The environmental data has been stored in the database system in accordance. Nevertheless, the data stored in the database gradually expanded due to long-term observation. The authors have tried to utilize the feature value hidden in spacious environmental data for the present dynamical analysis. Ide and Inoue (2005) have proposed the singular spectrum transformation (SST), which uses the eigenvalues (λ) and eigenvectors (U) for a characteristic matrix formed from a set of time series data and then verified that the SST is capable of detecting the change points from arbitrary time series data automatically and systematically. Okayasu et al. (2012) applied this method to evaluate the environmental dynamics for CO2 concentration in a tomato greenhouse.. In order to dissemination of environmental information and analysis result, in this study a change point analysis using SST mentioned above was adopted in a web-based application for detecting change points from the environmental time series data. The validity of the method and performance of web-application will be verified by using the environmental data stored in the Agri-eye database measured in the real greenhouse cultivation.

Keywords: change point analysis, singular spectrum transform (SST), dynamical analysis, web application, time series analysis

Submitted to International Symposium on Agricultural and Biosystem Engineering (ISABE) 2013

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