Reflectance images of effective wavelengths from hyperspectral imaging for identification of Fusarium head blight-infected wheat kernels combined with a residual attention convolution neural network

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

Download Full-text

Fumonisin B1 and zearalenone contamination of wheat in Croatia and influence of fungicide treatments

Phytoprotection ◽

10.7202/038984ar ◽

2010 ◽

Vol 90 (1) ◽

pp. 31-34 ◽

Cited By ~ 5

Author(s):

Dario Ivic ◽

Ana-Marija Domijan ◽

Maja Peraica ◽

Bogdan Cvjetkovic

Keyword(s):

Fusarium Head Blight ◽

Fumonisin B1 ◽

Wheat Grain ◽

Head Blight ◽

Fusarium Head Blight Severity ◽

Fungicide Efficacy ◽

Disease Pressure ◽

Wheat Kernels

In Croatia, a trial was conducted to determine the presence of theFusariummycotoxins fumonisin B1and zearalenone in wheat kernels and to evaluate the efficacy of nine fungicides on Fusarium head blight severity as well as fumonisin B1and zearalenone accumulation in wheat grain. Fumonisin B1and zearalenone were detected in all grain samples in mean concentrations ranging from 182.0 to 446.6 µg kg-1(fumonisin B1) and from 2.59 to 5.33 µg kg-1(zearalenone). No significant differences were found among fumonisin B1and zearalenone content in wheat grain for the different fungicide treatments. No correlation was revealed between Fusarium head blight severity and fumonisin B1or zearalenone content in wheat grain, nor between fungicide efficacy and fumonisin B1or zearalenone content in wheat grain. Under conditions of high disease pressure, efficacy of the fungicides was between 85.7% (tebuconazole + triadimefon) and 72.1% (carbendazim).

Download Full-text

Detection of Fusarium head blight contamination in wheat kernels by multivariate imaging

Food Control ◽

10.1016/j.foodcont.2015.01.048 ◽

2015 ◽

Vol 54 ◽

pp. 250-258 ◽

Cited By ~ 22

Author(s):

B. Jaillais ◽

P. Roumet ◽

L. Pinson-Gadais ◽

D. Bertrand

Keyword(s):

Fusarium Head Blight ◽

Head Blight ◽

Wheat Kernels

Download Full-text

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Remote Sensing ◽

10.3390/rs10030395 ◽

2018 ◽

Vol 10 (3) ◽

pp. 395 ◽

Cited By ~ 30

Author(s):

Xiu Jin ◽

Lu Jie ◽

Shuai Wang ◽

Hai Qi ◽

Shao Li

Keyword(s):

Neural Network ◽

Fusarium Head Blight ◽

Deep Neural Network ◽

Head Blight ◽

In The Wild ◽

Blight Disease

Download Full-text

Two Different Inoculation Methods Unveiled the Relative Independence of DON Accumulation in Wheat Kernels from Disease Severity on Spike after Infection by Fusarium Head Blight

Toxins ◽

10.3390/toxins13050353 ◽

2021 ◽

Vol 13 (5) ◽

pp. 353

Author(s):

Rong Wang ◽

Chen Hua ◽

Yi Hu ◽

Lei Li ◽

Zhengxi Sun ◽

...

Keyword(s):

Fusarium Head Blight ◽

Disease Severity ◽

Recombinant Inbred Lines ◽

Wheat Yield ◽

Head Blight ◽

Inoculation Methods ◽

High Severity ◽

Wheat Kernel ◽

Overall Resistance ◽

Wheat Kernels

Fusarium head blight (FHB) causes wheat yield loss and mycotoxin (deoxynivalenol, DON) accumulation in wheat kernel. Developing wheat cultivars with overall resistance to both FHB spread within a spike and DON accumulation in kernels is crucial for ensuring food security and food safety. Here, two relatively novel inoculation methods, bilateral floret inoculation (BFI) and basal rachis internode injection (BRII), were simultaneously employed to evaluate disease severity and DON content in kernels in a segregating population of recombinant inbred lines (RILs) developed from Ning 7840 (carrying Fhb1) and Clark (without Fhb1). Under both inoculation methods, four contrasting combinations of disease severity and DON content were identified: high severity/high DON (HSHD), high severity/low DON (HSLD), low severity/high DON (LSHD) and low severity/low DON (LSLD). Unexpectedly, the BRII method clearly indicated that disease severity was not necessarily relevant to DON concentration. The effects of Fhb1 on disease severity, and on DON concentrations, agreed very well across the two methods. Several lines carrying Fhb1 showed extremely higher severity and (or) DON content under both inoculation methods. The “Mahalanobis distance” (MD) method was used to rate overall resistance of a line by inclusion of both disease severity and DON content over both methods to select LSLD lines.

Download Full-text

Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging

Toxins ◽

10.3390/toxins11100556 ◽

2019 ◽

Vol 11 (10) ◽

pp. 556 ◽

Cited By ~ 8

Author(s):

Elias Alisaac ◽

Jan Behmann ◽

Anna Rathgeb ◽

Petr Karlovsky ◽

Heinz-Wilhelm Dehne ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Spectral Reflectance ◽

Near Infrared ◽

Fusarium Species ◽

Head Blight ◽

Wheat Varieties ◽

Fusarium Infection ◽

Mycotoxin Contamination ◽

Fungal Dna ◽

Wheat Kernels

Fusarium head blight (FHB) epidemics in wheat and contamination with Fusarium mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for Fusarium infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three Fusarium species. Kernels of two wheat varieties inoculated at anthesis with F. graminearum, F. culmorum, and F. poae were investigated. Hyperspectral images of kernels and flour were taken in the visible-near infrared (VIS-NIR) (400–1000 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges. The fungal DNA and mycotoxin contents were quantified. Spectral reflectance of Fusarium-damaged kernels (FDK) was significantly higher than non-inoculated ones. In contrast, spectral reflectance of flour from non-inoculated kernels was higher than that of FDK in the VIS and lower in the NIR and SWIR ranges. Spectral reflectance of kernels was positively correlated with fungal DNA and deoxynivalenol (DON) contents. In the case of the flour, this correlation exceeded r = −0.80 in the VIS range. Remarkable peaks of correlation appeared at 1193, 1231, 1446 to 1465, and 1742 to 2500 nm in the SWIR range.

Download Full-text

Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment

Remote Sensing ◽

10.3390/rs11202375 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2375 ◽

Cited By ~ 4

Author(s):

Dongyan Zhang ◽

Daoyong Wang ◽

Chunyan Gu ◽

Ning Jin ◽

Haitao Zhao ◽

...

Keyword(s):

Neural Network ◽

Fusarium Head Blight ◽

Reference Value ◽

Head Blight ◽

Convolutional Network ◽

Bee Colony ◽

Field Environment ◽

Severity Of The Disease ◽

The Cost ◽

Disease Grade

Fusarium head blight (FHB), one of the most important diseases of wheat, mainly occurs in the ear. Given that the severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is also polluted. In this study, a neural network (NN) method was proposed based on the red-green-blue (RGB) image to segment wheat ear and disease spot in the field environment, and then to determine the disease grade. Firstly, a segmentation dataset of single wheat ear was constructed to provide a benchmark for the segmentation of the wheat ear. Secondly, a segmentation model of single wheat ear based on the fully convolutional network (FCN) was established to effectively realize the segmentation of the wheat ear in the field environment. An FHB segmentation algorithm was proposed based on a pulse-coupled neural network (PCNN) with K-means clustering of the improved artificial bee colony (IABC) to segment the diseased spot of wheat ear by automatic optimization of PCNN parameters. Finally, the disease grade was calculated using the ratio of the disease spot to the whole wheat ear. The experimental results show that: (1) the accuracy of the segmentation model for single wheat ear constructed in this study is 0.981. The segmentation time is less than 1 s, indicating that the model can quickly and accurately segment wheat ear in the field environment; (2) the segmentation method of the disease spot performed under each evaluation indicator is improved compared with the traditional segmentation methods, and the accuracy is 0.925 in the disease severity identification. These research results can provide important reference value for grading wheat FHB in the field environment, which also can be beneficial for real-time monitoring of other crops’ diseases under near-Earth remote sensing.

Download Full-text