University of Oulu

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

Data analysis in moving windows for optimizing barley net blotch prediction

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Author: Ruusunen, Outi1; Jalli, Marja2; Jauhiainen, Lauri2;
Organizations: 1University of Oulu, Control Engineering, Environmental and Chemical Engineering Research Unit, Oulu, Finland
2Natural Resources Institute Finland, Jokioinen, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: Journal of advanced agricultural technologies, 2020
Publish Date: 2021-01-29


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.

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Series: Journal of advanced agricultural technologies
ISSN: 2373-423X
ISSN-E: 2301-3737
ISSN-L: 2373-423X
Volume: 7
Issue: 2
Pages: 38 - 42
DOI: 10.18178/joaat.7.2.38-42
Type of Publication: A1 Journal article – refereed
Field of Science: 415 Other agricultural sciences
Funding: The results were achieved during the MaDaKas project, financially supported by Ministry of Agriculture and Forestry of Finland. The authors are acknowledging The Ministry for financial support.
Copyright information: Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.