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Data Analysis in Moving Windows for Optimizing Barley Net Blotch Prediction

Outi Ruusunen 1, Marja Jalli 2, Lauri Jauhiainen 2, Mika Ruusunen 1, and Kauko Leiviskä1
1. University of Oulu, Control Engineering, Environmental and Chemical Engineering Research Unit, Oulu, Finland
2. Natural Resources Institute Finland, Jokioinen, Finland
Abstract—In modern agriculture, the pesticides and the need to decrease their use is under discussion. Optimization methods and modelling tools are important research areas in this context. In this paper, data analysis, feature generation and selection in moving windows have been utilized for the evaluation of net blotch risk in barley. Two different datasets: The open data from the Finnish Meteorological Institute and the historical observation of the net blotch severity in different fields in Finland are combined with feature generation techniques. T-test is then applied to select the most statistically suitable features for prediction the net blotch risk from weather measurements. Analysis proceeds in moving data windows to indicate the most informative time period to predict the risk of net blotch during the growing season. Results show that the selection of the proper time instance and the length of data window may enhance strongly the potential performance of prediction methods for risk analysis on plant disease.
Index Terms—advanced data analysis, moving window, barley net blotch, t-test, feature generation, IPM
Cite: Outi Ruusunen, Marja Jalli, Lauri Jauhiainen, Mika Ruusunen, and Kauko Leivisk, "Data Analysis in Moving Windows for Optimizing Barley Net Blotch Prediction," Journal of Advanced Agricultural Technologies, Vol. 7, No. 2, pp. 38-42, December 2020. Doi: 10.18178/joaat.7.2.38-42

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