Hyperspectral imaging facilitates early detection of Orobanche cumana below-ground parasitism on sunflower under field conditions

Abstract

• Early detection of O. cumana in soil subsurface facilitates site-specific control. • Hyperspectral imaging of host leaves, in field conditions, detects O. cumana at an early parasitism stage. • Leaf segmentation improves classification accuracy. • Multispectral data is sufficient for classification of parasitized plants. Sunflower broomrape ( Orobanche cumana ) is a root parasitic weed that severely limits sunflower yield in large areas of Europe and Asia. Early detection of the parasite can facilitate site-specific control of this weed. However, most of its life-cycle takes place in the soil sub-surface and by the time that O . cumana shoots emerge, the damage to the crop is irreversible. The main aim of this study was to evaluate the potential use of hyperspectral imaging for the early detection of parasitism by monitoring changes in spectra obtained from the host plants. A field experiment was conducted on infested and non-infested sunflower plants, imaged by a ground-based hyperspectral camera at two early parasitism stages that are relevant for herbicide application. A logistic regression model was used to classify infected and non-infected plants, 31 and 38 days after sunflower planting, with 76 and 89% accuracy, respectively. A partial dataset, containing only 10 spectral bands of the hyperspectral dataset, gave 69 and 82% accuracy, indicating the potential of multi-spectral sensors for the detection task. Sampling pixels from specific sunflower leaf segments improved the classification compared to non-specific sampling. This study thus contributes to establishing a basis for future development of site-specific weed management of O. cumana and of other broomrape species.