Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Qualitative Parameter Analysis for Botrytis cinerea Forecast Modelling Using IoT Sensor Networks

Version 1 : Received: 2 August 2021 / Approved: 3 August 2021 / Online: 3 August 2021 (11:20:03 CEST)

How to cite: Gligoric, N.; Popovic, T.; Drajic, D.; Gajinov, S.; Krco, S. Qualitative Parameter Analysis for Botrytis cinerea Forecast Modelling Using IoT Sensor Networks. Preprints 2021, 2021080072 (doi: 10.20944/preprints202108.0072.v1). Gligoric, N.; Popovic, T.; Drajic, D.; Gajinov, S.; Krco, S. Qualitative Parameter Analysis for Botrytis cinerea Forecast Modelling Using IoT Sensor Networks. Preprints 2021, 2021080072 (doi: 10.20944/preprints202108.0072.v1).

Abstract

This paper presents the evaluation of a fungal disease forecast model in vineyards for qualitative parameter analysis using the data from off the shelf sensors, i.e. temperature and air relative humidity, rain precipitation, and leaf wetness. The rules for the fungal disease models are digitalized as a decision support tool that serve as an indicator to farmers for the need of spraying of the chemical substances to ensure the best growing condition and suppress the level of parasites. The temperature and humidity contexts are used interchangeably in practice to detect the risk of the disease occurrence. By taking into account a number of influences on these parameters collected from the shelf sensors, new topics for research in the multidimensional field of precision agriculture emerge. In this study, the impact of the humidity is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each humidity parameter has it’s own threshold that triggers the second step of the disease modeling - risk index based on the temperature. The research showed that for humidity a low-cost relative humidity sensor can detect in average 14.61% risk values, a leaf wetness sensor an additional 3.99% risk cases, and finally, a precipitation sensor will detect only an additional 0.59% risk cases.

Keywords

IoT; Fungal disease forecast; Botrytis cinerea; Precise agriculture; Decision support

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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