Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology

Julien Morel,Francoise Carreel,Nathalie Gorretta,Adel Bakache,Ryad Bendoula,Jean-Baptiste Féret,Sylvain Jay

doi:10.1038/s41598-018-34429-0

Abstract

The detection of plant diseases, including fungi, is a major challenge for reducing yield gaps of crops across the world. We explored the potential of the PROCOSINE radiative transfer model to assess the effect of the fungus Pseudocercospora fijiensis on leaf tissues using laboratory-acquired submillimetre-scale hyperspectral images in the visible and near-infrared spectral range. The objectives were (i) to assess the dynamics of leaf biochemical and biophysical parameters estimated using PROCOSINE inversion as a function of the disease stages, and (ii) to discriminate the disease stages by using a Linear Discriminant Analysis model built from the inversion results. The inversion results show that most of the parameter dynamics are consistent with expectations: for example, the chlorophyll content progressively decreased as the disease spreads, and the brown pigments content increased. An overall accuracy of 78.7% was obtained for the discrimination of the six disease stages, with errors mainly occurring between asymptomatic samples and first visible disease stages. PROCOSINE inversion provides relevant ecophysiological information to better understand how P. fijiensis affects the leaf at each disease stage. More particularly, the results suggest that monitoring anthocyanins may be critical for the early detection of this disease.

Highlights

Plant diseases are a major issue for food and crop production, and can lead to yield gaps between potential and actual productions[1]
The results shown in previous sections demonstrate that PROCOSINE (i) provides dynamics that are consistent with physiological expectations and (ii) is useful to investigate the effects of P. fijiensis and, more generally, of plant diseases on leaf tissues
The coupled PROSPECT-D + COSINE model (PROCOSINE) was used to study the response of structure and chemistry of banana leaves when infected with black leaf streak disease (BLSD) caused by the fungus P. fijiensis

Summary

Introduction

Plant diseases are a major issue for food and crop production, and can lead to yield gaps between potential and actual productions[1]. Mahlein et al.[14] used hyperspectral signatures of healthy and infected sugar beet leaves acquired in laboratory conditions with a non-imaging spectrometer to develop specific disease indices Such indices may be affected by directional effects induced by the leaf surface, and the obtained empirical relationships may show moderate extrapolative abilities, e.g., when applied to other crops or different sensors. Another widely used method to detect foliar diseases based on remote-sensing data relies on the development of multivariate regression models based on the whole reflectance spectrum. Their results showed that it was possible to select optimal wavelengths in the VNIR range in order to discriminate healthy leaves from those contaminated at incubation (i.e., early) and symptomatic stages

Methods

Results

Discussion

Conclusion