remote sensing Article Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning Simon Appeltans 1 , Jan G. Pieters 2 and Abdul M. Mouazen 1,* 1 Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium;
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[email protected] * Correspondence:
[email protected] Abstract: Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400–1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection Citation: Appeltans, S.; Pieters, J.G.; techniques and preprocessing strategies.