Botrytis cinerea affects plant yield and quality. Many Botrytis species are morphologically similar leading to difficulty in pathogen identification. Spectroscopy can be used to identify pathogenic fungi. This study describes a novel method for fungal characterization. Here, we determined the spectral signatures of different B. cinerea isolates as well as various fungal genera. A unique spectral pattern was investigated at both genus and isolate level. The short wave infrared II (2055 - 2315 nm) provided the best discrimination between the fungal samples observed. Moreover, the spectral analysis was performed on non-transformed data and investigated significant differences among fungal genera as well as B. cinerea isolates, while the results investigated high similarity among replicates of the same isolate of B. cinerea. The results of each spectral test were obtained reproducibly without an expensive cost consumable during sample preparation and measurements. This innovative approach would allow us to identify, discriminate and classify fungi rapidly and inexpensively at the genus, species and isolate level.
Botrytis cinerea Pers.:Fr. (teleomorph: Botryotinia fuckeliana (de Bary) Whetzel), the cause of gray mold, is a necrotrophic pathogen causing economically important preand postharvest diseases in at least 235 plant species. One of the major infections it causes, grape and strawberry fruit rot, leads to reduced quality and quantity of fruit. Although variation in morphology and aggressiveness characteristics had been reported between isolates of Botrytis spp. [1,2], many species are morphologically similar [
Remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with the object. Hyperspectral sensors collect numerous wavelengths of reflected or transmitted radiance in many contiguous wavelengths. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify various objects by means of propagated signals [10,11]. Certain objects leave unique “fingerprints” across the electromagnetic spectrum (EM). These “fingerprints” are known as spectral signatures and enable identification of the scanned organism. Spectral signatures are the specific combination of reflected and absorbed electromagnetic radiation at varying wavelengths which can uniquely identify an object. Hyperspectral data contain information that relates to important biochemical properties in the cell [12- 16].
The measurements can be made with various instruments, including spectroradiometers. Hyperspectral remote-sensing approaches have been proposed for various agricultural purposes such as the identification and classification of plant species, estimation of plant yield, detection and quantification of crop diseases [16,17], extraction of crop biophysical parameters [18-20], application of fungicides and plant nutrition [
The modified selective medium, m1KERS, was based on medium compositions previously developed [6,34,35] without adding pentachloronitrobenzene component, the basis of KERS medium [36,37]. The composition of this medium (g/L distilled water) is: glucose, 20 g; NaNO3, 1 g; KH2PO4, 1.2 g; MgSO4·7H2O, 0.2 g; KCl, 0.15 g; agar, 25 g. This medium was autoclaved at 121˚C for 20 min. After cooling to 65˚C, the following ingredients were added: chloramphenicol, 0.05 g; tannic acid, 5 g; CuSO4, 2.2 g; Cabrio Top fungicide (Pyraclostrobin), 0.1 g. Different organs from strawberry (Var. Florida & 029) and grape (Var. Superior & Flame) were tested for Botrytis infection (
mycelium growth and the brown halo formation of B. cinerea were tested in comparison with other fungal genera used as negative controls (Aspergillus sp., Rhizopus sp., Penicillium italicum, Penicillium digitatum, Fusarium sp., Alternaria sp. and Rhizoctonia sp.). The samples were examined daily for development of B. cinerea isolates which were transferred to potato dextrose agar (PDA; Difco) and incubated for 21 days at 23˚C. Three replicates were prepared for each fungal culture.
Spectral data were collected using an ADS field spectroradiometer (Boulder, CO, USA). It has the characteristic of being nearly 100% reflective within the wavelength range of 350 nm to 2500 nm. The spectral resolution of this instrument was 3 nm at wavelengths between 350 - 1000 nm, and 10 nm for longer wavelengths, with spectral sampling intervals of 1.4 and 2 nm, respectively [
The protocol used for the collection of spectral data was based on measuring radiance from a Spectralon® panel (North Sutton, NH, USA). A designed probe was attached to the instrument’s fiber-optic cable to ensure standardized environmental conditions for reflectance measurement. The fiber-optic cable provides the flexibility to adapt the instrument to a wide range of applications. The measurements were performed by holding the pistol grip by hand. Bare foreoptic 25 degrees used for outdoor measurements resulting circular field of view with 3 cm diameter as measurements were taken at 3 cm height in nadir position 90 degrees over target surface. For the current study and as recommended in the instructions of using the device in outdoor measurements, reference panel is closer to the foreoptics. For outdoor optimization, the Spectralon® was tilted directly towards the sun during optimization. Dark-current is systematic noise from the instrument electronics and detectors. In the current study, the spectroradiometer includes mechanically controlled shutters and software for recording and automatically subtracting dark-current. After calibrating the spectroradiometer, the measurements were performed under sun light condition at 11 am-1 pm according to manufacturer instructions by holding the pistol grip. The reflectance was averaged for each 10 (nm) resulted in a specific number of spectral bands (NSB) for each spectral zone (NSZ) with a total of 138 spectral bands. The number of spectral bands in each spectral zone is represented in
The spectral measurement was performed on seven different fungi (Aspergillus sp., Botrytis cinerea, Penicillium italicum, Penicillium digitatum, Fusarium sp., Al-
ternaria sp. and Rhizoctonia sp.) and thirteen isolates of Botrytis cinerea (BCS1, BCS15, BCG3, BCG4, BCG7, BCG12, BCG13, BCG16, BCG17, BCG19, BCG24, BCG27, BCG30). Each fungal culture, aged 21 days, has three replicates in an individual petri dish. The spectral measurement for each fungal culture was measured three times (three observations at the same time) and then we have taken the average of these three measurements to represent the spectral curve of each fungus and then to show the variation of the spectral patterns among different fungal cultures.
Spectral zones that represent the atmospheric windows (portions of the electromagnetic reflectance that include data noise because of the relative air humidity) were removed. Spectral pattern of each measured sample was identified. Data were divided into six different spectral portions as follows: blue (350 - 440 nm), green (450 - 540 nm), red (550 - 750 nm), NIR (760 - 1000 nm), SWIR I (1010 - 1775 nm) and SWIR II (2055 - 2315 nm).
Statistical analysis was performed using SAS program [
Before conducting the Statistical analysis, the distribution of the spectral responses was assumed to be normal under the central limit theorem and the equality of statistical variances was verified for every spectral location. Then, the hypothesis test was carried out using one way ANOVA at every spectral location between 350 nm and 2500 nm (a total of 2150 spectral bands).
The Tukey’s significant difference test was applied to define ranges that significantly differ along the 2150 bands. The selected bands then formed the basis for a linear discriminant analysis (LDA) that classified the different fungal genera and B. cinerea isolates. We applied a one-way ANOVA to filter out wavebands that did not differ significantly in mean reflectance among different fungi. A one-way ANOVA was carried out on fungi as the independent factor for each of the 2150 wavebands. We considered p < 0.05 as an indication for the significant differences as the mean reflectance of at least two of the different fungi differed in the tested band; all bands meeting this criterion were included in the LDA. One potential criticism of this band selection procedure is that the results of tests on adjacent bands are not statistically independent. However, our objective in applying ANOVA was not to test hypotheses about differences within specific bands; rather, we were seeking to eliminate bands from the analysis that provided no useful information for discriminating fungal reflectance patterns, and thereby reduce the number of analyzed bands to a level that would be operational for LDA. This band selection procedure was performed on all tested samples.
Different organs from both strawberry and grape (
An average measurement of each fungal culture was taken (see materials and methods) to identify the spectral reflectance pattern which was averaged for each 10 nm resulted in a specific number of spectral bands for each spectral zone (
Tukey’s HSD test was carried out to show the variation between the different fungi in the six different spectral zones (blue, green, red, NIR, SWIR I and SWIR II).
The standard deviation of the spectral reflectance of all analyzed fungi were indicated within the six spectral zones (
An average measurement of each isolate of B. cinerea was taken (see Materials and Methods) to identify the spectral reflectance pattern. Different isolates were tested for spectral reflectance using the six spectral zones (blue, red, near infrared and SWIR I) which showed similarity in spectral data among isolates (Data not shown). While, the spectral analysis of each isolate using SWIR II at 2055 - 2315 nm showed similar forms of signature which could be divided into 5 groups depending on their footprint similarity: group 1 which contained the majority of B. cinerea isolates: BCG30, BCG4, BCS15, BCG7, BCG24, BCG12 (
Indeed, the current spectral measurements and data analysis demonstrated result reproducibility confirming the accuracy of using the minimal range of SWIR II (2055 - 2315 nm) for fungal discrimination. In fact, previous studies had demonstrated a large spectral range (Vis-NIR, 400 - 2498 nm) to distinguish fungal species by spectroscopy [
methods that depend on cell composition like proteins/ biological materials [45-50] nor transforming data [22- 25]. The results of this study permit us to use spectroradiometer at such limited spectral zone for direct fungal classification. Other advanced molecular technique like PCR approach could not directly classify the tested unknown pathogen although it has many advantages like rapidity, specificity and sensitivity [
This project was supported financially by the Science and Technology Development Fund (STDF), Egypt, Grant No 2131. This study was registered as a patent n. 1521/2012 in Egypt. We thank the research staff of the Agricultural applications at the Egyptian National Authority for Remote Sensing and Space Sciences.