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.

Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance

“…However, these wavelengths were related to pigments, such as carotenoids and chlorophyll [36]. One study has shown no improvement in the models when using the blue and green spectral regions for input information [18]. In this study, we found that the leaf hyperspectral reflectance in the green region was poorly correlated with the soybean FLS classification.…”

“…Hyperspectral technique can be also used to conduct an in-depth examination of crop characters, such as crop cell structure, chlorophyll content, moisture content, trace element content, and light reflection and absorption characteristics. This may not be achieved using multispectral data, which have the relative coarse bandwidths [18]. Many studies have shown the feasibility and potential of hyperspectral reflectance data for evaluating the degree of crop diseases such as rice glume blight disease [19], black bean yellow mosaic disease [20], and citrus greening [21].…”

“…PCA can also reduce the high dependence on adjacent wavebands. Hence, several studies have reportedly employed PCA for data compression of hyperspectral data [18,48,49]. Reduction in redundancy and correlation improves the accuracy and reliability of the analysis results of hyperspectral data.…”

Predicting some quality factors of annual ryegrass (Lolium multiflorum lam.) by means of spectral reflectance values

“…The spectral reflectance measurements were taken under laboratory conditions in a dark environment admitting no light from outside. For the measurements, a spectroradiometer (ASD Inc., Boulder, CO, USA) (20)(21)(22) that can make reflectance measurements between the wavelengths of 325-1075 nm of the electromagnetic spectrum and a contact probe (plant probe) were used. Contact probe was attached to the spectroradiometer ( 23) and contain a 100 W halogen lamp as an artificial light source (24).…”

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

“…Crop yields predictions based on soil fertility parameters and metrological data was possible by using ANNs, thanks to its abstraction capability (Dahikar and Rode, 2014). In plant diseases, an ANN was able to estimate the severity of angular leaf spot in bean cultures, using leaf and canopy hyperspectral reflectance spectra (Martínez-Martínez et al, 2018). The use of ANNs have a widespread future in the development of new techniques, and there is more room for future research and applications of ANNs (Darji et al, 2015;Ghanbarzadeh et al, 2009;Laaboudi et al, 2012).…”