Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops.

Abstract

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630-850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440-850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

Plant phenotyping relevance

葉・キャノピーのハイパースペクトル反射から植物病害の重症度を推定する手法を開発・評価しており、病害表現型の取得と推定が研究の中心である。

Code and data availability

The paper states its spectral reflectance data, RGB images, and repository file are provided as Supporting Information (S1 Text), but no public URL, deposit, or authors' code release is given in the supplied blocks; the only URLs present are funding agency and reference links, none pointing to the paper's phenotyping数据

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