Detection of leek white tip disease under field conditions using hyperspectral proximal sensing and supervised machine learning

Abstract Leek white tip disease is one of the key yield-limiting factors in leek production. It leads to socio-economic and environmental costs due to the need for fungicide applications. Precision agriculture aims at reducing these costs, while maintaining or improving farmer revenues. To be able to apply variable rate fungicides, in-field disease detection is necessary. In this work, a disease detection methodology was created that can detect early, pre-symptomatic white tip disease symptoms in field conditions with millimetre resolution. A hyperspectral training library was constructed containing 29,744 spectra of healthy leek, weed plants, diseased leek plants and soil. Soil pixels were removed from hyperspectral images by means of LDA classification, followed by a custom noise filter algorithm. Then, a logistic regression supervised machine learning classifier was trained with five classes: healthy plant material, early (pre-visual) disease, moderate disease, severe disease and fully developed disease. The overall accuracy of the disease detection model was 96.74%. Model diagnostics showed a low likelihood of classifying diseased pixels as healthy (3.59%) and a low likelihood of falsely classifying healthy pixels as diseased (0.77%).