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International Conference on Intelligent Decision Technologies

IDT 2020: Intelligent Decision Technologies pp 79–89Cite as

Artificial Intelligence Technique in Crop Disease Forecasting: A Case Study on Potato Late Blight Prediction

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 193))

Abstract

Crop diseases are strongly affected by weather and environmental factors. Weather fluctuations represent the main factors that lead to potential economic losses. The integration of forecasting models based on weather data can provide a framework for agricultural decision-making able to suggest key information for overcoming these problems. In the present work, we propose a new artificial intelligence approach to forecast potato late blight disease in the Sardinia region and a novel technique to express a crop disease risk. The experiments conducted are based on historical weather data as temperature, humidity, rainfall, speed wind, and solar radiation collected from several locations over 4 year (2016–2019). The tests were aimed to determine the usefulness of the support vector machine classifier to identify crop–weather–disease relations for potato crops and the relative possible outbreak. The results obtained show that temperature, humidity, and speed wind play a key role in the prediction.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Gianni Fenu & Francesca Maridina Malloci

Authors
  1. Gianni Fenu
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  2. Francesca Maridina Malloci
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Corresponding author

Correspondence to Gianni Fenu .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. KES International Research, UK

    Robert J. Howlett

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Fenu, G., Malloci, F.M. (2020). Artificial Intelligence Technique in Crop Disease Forecasting: A Case Study on Potato Late Blight Prediction. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_7

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