Search

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.

Hyperspectral imaging is an emergent technique in viticulture that can potentially detect bacterial diseases in a non-destructive manner. However, the main problem is to handle the substantial amount of information obtained from this type of data, for which reliable data analysis tools are necessary. In this work, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) is proposed to detect the flavescence dorée grapevine disease from hyperspectral imaging. The main purpose of MCR-ALS in this work was to provide chemically meaningful basic spectral signatures and distribution maps of the constituents needed to describe both healthy and infected leaf images by flavescence dorée. MCR scores (distribution maps) were used as the starting information for FDA to distinguish between healthy and infected pixels/images. Such an approach is presumably more powerful than the direct use of FDA on the raw imaging data, since MCR scores are compressed and noise-filtered information on pixel properties, which makes them more suitable for discrimination analysis. High levels of correct pixel discrimination rates (CR = 85.1%) for the MCR-ALS/FDA discrimination model were obtained. The model presents a lesser ability to determine infected leaves than healthy leaves. Nevertheless, only two images were misclassified. Therefore, the proposed strategy constitutes a good approach for the detection of flavescence dorée that could be potentially used to detect other phytopathologies. [ABSTRACT FROM AUTHOR]

Drip irrigation is one of the most efficient irrigation techniques, but it is susceptible to dripper clogging. This study proposes a novel and non-destructive method based on visible and near infrared (Vis/NIR) spectroscopy coupled with chemometric methods for the discrimination and thickness estimation of physical and chemical fouling in drip-irrigation systems. Four representative materials linked to physical and chemical clogging (kaolin, bentonite, sand and calcium carbonate) at different thicknesses were selected to illustrate the potential of the approach. Partial least squares regression (PLSR) and its modification partial least squares with discriminant analysis (PLS-DA) were selected for the modelling of clogging materials. The PLS-DA model was able to predict with 96.97% accuracy all classes of materials. The PLSR models were able to estimate fouling thickness with relative prediction errors comprised between 134 μm and 164 μm. This difference appears mainly to be due to the physical properties of the selected materials. This prediction accuracy enabled the estimation of the clogging thickness between 10 and 21% of dripper channel coverage depending on the dripper channel section and the material under study. The proposed method offers an appropriate approach for clogging studies in drip irrigation systems that could be transferred to field applications. • Studied clays, sand and calcium carbonate represent physical and chemical clogging. • Visible/Near infrared (Vis/NIR) spectroscopy was coupled with chemometric methods. • Thickness estimation was performed using Partial Least Squares Regression (PLSR). • Clogging material was discriminated using PLS with Discriminant Analysis (PLS-DA). • Vis/NIR spectroscopy is promising for clogging study in drip irrigation. [ABSTRACT FROM AUTHOR]

This article proposes a generic framework to process jointly the spatial and spectral information of hyperspectral images. First, sub-images are extracted. Then each of these sub-images follows two parallel workflows, one dedicated to the extraction of spatial features and the other dedicated to the extraction of spectral features. Finally, the extracted features are merged, producing as many scores as sub-images. Two applications are proposed, illustrating different spatial and spectral processing methods. The first one is related to the characterization of a teak wood disk, in an unsupervised way. It implements tensors of structure for the spatial branch, simple averaging for the spectral branch and multi-block principal component analysis for the fusion process. The second application is related to the early detection of apple scab on leaves. It implements co-occurrence matrices for the spatial branch, singular value decomposition for the spectral branch and multiblock partial least squares discriminant analysis for the fusion process. Both applications demonstrate the interest of the proposed method for the extraction of relevant spatial and spectral information and show how promising this new approach is for hyperspectral imaging processing. • A generic method proposed to fuse spatial and spectral information. • The workflow combines spatial and spectral features with multi-block methods. • The teak disc case study illustrates the unsupervised fusion of spatial and spectral data. • Apple scab case study illustrates supervised fusion of spatial and spectral data. [ABSTRACT FROM AUTHOR]