scholarly journals Volatile Organic Compound Profiles From Wheat Diseases Are Pathogen-Specific and Can Be Exploited for Disease Classification

2022 ◽  
Vol 12 ◽  
Author(s):  
Andrea Ficke ◽  
Belachew Asalf ◽  
Hans Ragnar Norli
Keyword(s):  
Pure Culture ◽  
Fusarium Culmorum ◽  
Fungal Species ◽  
Plant Diseases ◽  
Disease Classification ◽  
Diagnostic Tools ◽  
Head Blight ◽  
Wheat Varieties ◽  
Wheat Diseases ◽  
Volatile Organic

Plants and fungi emit volatile organic compounds (VOCs) that are either constitutively produced or are produced in response to changes in their physico-chemical status. We hypothesized that these chemical signals could be utilized as diagnostic tools for plant diseases. VOCs from several common wheat pathogens in pure culture (Fusarium graminearum, Fusarium culmorum, Fusarium avenaceum, Fusarium poae, and Parastagonospora nodorum) were collected and compared among isolates of the same fungus, between pathogens from different species, and between pathogens causing different disease groups [Fusarium head blight (FHB) and Septoria nodorum blotch (SNB)]. In addition, we inoculated two wheat varieties with either F. graminearum or P. nodorum, while one variety was also inoculated with Blumeria graminis f.sp. tritici (powdery mildew, PM). VOCs were collected 7, 14, and 21 days after inoculation. Each fungal species in pure culture emitted a different VOC blend, and each isolate could be classified into its respective disease group based on VOCs with an accuracy of 71.4 and 84.2% for FHB and SNB, respectively. When all collection times were combined, the classification of the tested diseases was correct in 84 and 86% of all cases evaluated. Germacrene D and sativene, which were associated with FHB infection, and mellein and heptadecanone, which were associated with SNB infection, were consistently emitted by both wheat varieties. Wheat plants infected with PM emitted significant amounts of 1-octen-3-ol and 3,5,5-trimethyl-2-hexene. Our study suggests that VOC blends could be used to classify wheat diseases. This is the first step toward a real-time disease detection in the field based on chemical signatures of wheat diseases.

scholarly journals The Role of Fungi in the Cocoa Production Chain and the Challenge of Climate Change

2021 ◽  
Vol 7 (3) ◽  
pp. 202
Author(s):  
Johannes Delgado-Ospina ◽  
Junior Bernardo Molina-Hernández ◽  
Clemencia Chaves-López ◽  
Gianfranco Romanazzi ◽  
Antonello Paparella
Keyword(s):  
Climate Change ◽  
Fungal Infections ◽  
Close Association ◽  
Pathogenic Fungi ◽  
Fungal Species ◽  
Mitigation Strategies ◽  
Plant Diseases ◽  
Production Chain ◽  
Cocoa Production

Background: The role of fungi in cocoa crops is mainly associated with plant diseases and contamination of harvest with unwanted metabolites such as mycotoxins that can reach the final consumer. However, in recent years there has been interest in discovering other existing interactions in the environment that may be beneficial, such as antagonism, commensalism, and the production of specific enzymes, among others. Scope and approach: This review summarizes the different fungi species involved in cocoa production and the cocoa supply chain. In particular, it examines the presence of fungal species during cultivation, harvest, fermentation, drying, and storage, emphasizing the factors that possibly influence their prevalence in the different stages of production and the health risks associated with the production of mycotoxins in the light of recent literature. Key findings and conclusion: Fungi associated with the cocoa production chain have many different roles. They have evolved in a varied range of ecosystems in close association with plants and various habitats, affecting nearly all the cocoa chain steps. Reports of the isolation of 60 genera of fungi were found, of which only 19 were involved in several stages. Although endophytic fungi can help control some diseases caused by pathogenic fungi, climate change, with increased rain and temperatures, together with intensified exchanges, can favour most of these fungal infections, and the presence of highly aggressive new fungal genotypes increasing the concern of mycotoxin production. For this reason, mitigation strategies need to be determined to prevent the spread of disease-causing fungi and preserve beneficial ones.

scholarly journals Volatile organic compound patterns predict fungal trophic mode and lifestyle

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuan Guo ◽  
Werner Jud ◽  
Fabian Weikl ◽  
Andrea Ghirardo ◽  
Robert R. Junker ◽  
...  
Keyword(s):  
Organic Compound ◽  
Fungal Species ◽  
Chemical Diversity ◽  
Phenotypic Integration ◽  
Ecological Studies ◽  
Volatile Organic ◽  
Ecological Importance ◽  
Fungal Interactions ◽  
Computational Analyses ◽  
Different Levels

AbstractFungi produce a wide variety of volatile organic compounds (VOCs), which play central roles in the initiation and regulation of fungal interactions. Here we introduce a global overview of fungal VOC patterns and chemical diversity across phylogenetic clades and trophic modes. The analysis is based on measurements of comprehensive VOC profiles of forty-three fungal species. Our data show that the VOC patterns can describe the phyla and the trophic mode of fungi. We show different levels of phenotypic integration (PI) for different chemical classes of VOCs within distinct functional guilds. Further computational analyses reveal that distinct VOC patterns can predict trophic modes, (non)symbiotic lifestyle, substrate-use and host-type of fungi. Thus, depending on trophic mode, either individual VOCs or more complex VOC patterns (i.e., chemical communication displays) may be ecologically important. Present results stress the ecological importance of VOCs and serve as prerequisite for more comprehensive VOCs-involving ecological studies.

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

Chemotype and aggressiveness evaluation of Fusarium graminearum and Fusarium culmorum isolates from wheat fields in Wisconsin

Plant Disease ◽  
2021 ◽  
Author(s):  
Brian Mueller ◽  
Carol Groves ◽  
Damon L. Smith
Keyword(s):  
Growth Rate ◽  
Fusarium Head Blight ◽  
Fusarium Graminearum ◽  
Fusarium Culmorum ◽  
Head Blight ◽  
Growing Seasons ◽  
Progress Curve ◽  
Sporulation Capacity ◽  
Derivatives Of

Fusarium graminearum commonly causes Fusarium head blight (FHB) on wheat, barley, rice, and oats. Fusarium graminearum produces nivalenol and deoxynivalenol (DON) and forms derivatives of DON based on its acetylation sites. The fungus is profiled into chemotypes based on DON derivative chemotypes (3 acetyldeoxynivalenol (3ADON) chemotype; 15 acetyldeoxynivalenol (15ADON) chemotype) and/or the nivalenol (NIV) chemotype. The current study assessed the Fusarium population found on wheat and the chemotype profile of the isolates collected from 2016 and 2017 in Wisconsin. Fusarium graminearum was isolated from all locations sampled in both 2016 and 2017. Fusarium culmorum was isolated only from Door County in 2016. Over both growing seasons, 91% of isolates were identified as the 15ADON chemotype while 9% of isolates were identified as the 3ADON chemotype. Aggressiveness was quantified by area under disease progress curve (AUDPC). The isolates with the highest AUDPC values were from the highest wheat producing cropping districts in the state. Deoxynivalenol production in grain and sporulation and growth rate in vitro were compared to aggressiveness in the greenhouse. Our results showed that 3ADON isolates in Wisconsin were among the highest in sporulation capacity, growth rate, and DON production in grain. However, there were no significant differences in aggressiveness between the 3ADON and 15ADON isolates. The results of this research detail the baseline frequency and distribution of 3ADON and 15ADON chemotypes observed in Wisconsin. Chemotype distributions within populations of F. graminearum in Wisconsin should continue to be monitored in the future.

scholarly journals Description of a Novel Mycovirus in the Phytopathogen Fusarium culmorum and a Related EVE in the Yeast Lipomyces starkeyi

Viruses ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 523
Author(s):  
Mathieu Mahillon ◽  
Gustavo Romay ◽  
Charlotte Liénard ◽  
Anne Legrève ◽  
Claude Bragard
Keyword(s):  
Retention Rate ◽  
Fusarium Culmorum ◽  
Fungal Species ◽  
Dual Culture ◽  
Isogenic Line ◽  
Biological Characterization ◽  
Lipomyces Starkeyi ◽  
Fungal Host ◽  
Dsrna Genome ◽  
Novel Virus

A new mycovirus was found in the Fusarium culmorum strain A104-1 originally sampled on wheat in Belgium. This novel virus, for which the name Fusarium culmorum virus 1 (FcV1) is suggested, is phylogenetically related to members of the previously proposed family ‘’Unirnaviridae’’. FcV1 has a monopartite dsRNA genome of 2898 bp that harbors two large non-overlapping ORFs. A typical -1 slippery motif is found at the end of ORF1, advocating that ORF2 is translated by programmed ribosomal frameshifting. While ORF2 exhibits a conserved replicase domain, ORF1 encodes for an undetermined protein. Interestingly, a hypothetically transcribed gene similar to unirnaviruses ORF1 was found in the genome of Lipomyces starkeyi, presumably resulting from a viral endogenization in this yeast. Conidial isolation and chemical treatment were unsuccessful to obtain a virus-free isogenic line of the fungal host, highlighting a high retention rate for FcV1 but hindering its biological characterization. In parallel, attempt to horizontally transfer FcV1 to another strain of F. culmorum by dual culture failed. Eventually, a screening of other strains of the same fungal species suggests the presence of FcV1 in two other strains from Europe.

scholarly journals Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2281 ◽  
Author(s):  
Anne-Katrin Mahlein ◽  
Elias Alisaac ◽  
Ali Al Masri ◽  
Jan Behmann ◽  
Heinz-Wilhelm Dehne ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Hyperspectral Imaging ◽  
Optical Sensors ◽  
Fusarium Culmorum ◽  
Maximum Temperature ◽  
Support Vector ◽  
Head Blight ◽  
Maximum Temperature Difference ◽  
Simple Ratio

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.

scholarly journals The Influence of the Dilution Rate on the Aggressiveness of Inocula and the Expression of Resistance against Fusarium Head Blight in Wheat

Plants ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 943 ◽  
Author(s):  
Beata Toth ◽  
Andrea Gyorgy ◽  
Monika Varga ◽  
Akos Mesterhazy
Keyword(s):  
Fusarium Head Blight ◽  
Dilution Rate ◽  
Fusarium Graminearum ◽  
Fusarium Culmorum ◽  
Disease Index ◽  
Head Blight ◽  
Important Trait

In previous research, conidium concentrations varying between 10,000 and 1,000,000/mL have not been related to any aggressiveness test. Therefore, two Fusarium graminearum and two Fusarium culmorum isolates were tested in the field on seven genotypes highly differing in resistance at no dilution, and 1:1, 1:2, 1:4, 1:8, and 1:16 dilutions in two years (2013 and 2014). The isolates showed different aggressiveness, which changed significantly at different dilution rates for disease index (DI), Fusarium-damaged kernels (FDK), and deoxynivalenol (DON). The traits also had diverging responses to the infection. The effect of the dilution could not be forecasted. The genotype ranks also varied. Dilution seldomly increased aggressiveness, but often lower aggressiveness occurred at high variation. The maximum and minimum values varied between 15% and 40% for traits and dilutions. The reductions between the non-diluted and diluted values (total means) for DI ranged from 6% and 33%, for FDK 8.3–37.7%, and for DON 5.8–44.8%. The most sensitive and most important trait was DON. The introduction of the aggressiveness test provides improved regulation compared to the uncontrolled manipulation of the conidium concentration. The use of more isolates significantly increases the credibility of phenotyping in genetic and cultivar registration studies.

scholarly journals Resistance to Fusarium head blight in spring barley

2011 ◽  
Vol 47 (No. 2) ◽  
pp. 58-63 ◽  
Author(s):  
J. Chrpová ◽  
V. Šíp ◽  
L. Štočková ◽  
L. Stemberková ◽  
L. Tvarůžek
Keyword(s):  
Fusarium Head Blight ◽  
Spring Barley ◽  
Fusarium Culmorum ◽  
Grain Weight ◽  
Head Blight ◽  
The Czech Republic ◽  
Disease Symptoms ◽  
Barley Cultivars ◽  
Yield And Quality ◽  
Medium Resistance

Fusarium head blight (FHB) is a fungal disease causing substantial yield and quality losses in barley. Genetic variation in deoxynivalenol (DON) content and and important yield traits in response to FHB were studied in 44 spring barley cultivars for two years following artificial inoculation with Fusarium culmorum under field conditions. The analysis of variance revealed that the largest effect on DON content and simultaneously on the reduction of thousand grain weight and grain weight per spike were due to the environmental conditions of the year, while the visual disease symptoms depended on the cultivars to a larger extent. All these traits were significantly interrelated. The most resistant cultivars Murasski mochi, Nordic, Krasnodarskij 35, Krasnodarskij 95, Nordus, and Usurijskij 8, together with the resistant check Chevron, showed the lowest DON content, the lowest expression of disease symptoms and the lowest reduction of TGW and GWS. However, most spring barley cultivars registered in the Czech Republic in recent years expressed susceptibility or medium resistance and were considerably affected by the disease. This increases the importance of breeding barley for resistance to FHB.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
Author(s):  
Dor Oppenheim ◽  
Guy Shani ◽  
Orly Erlich ◽  
Leah Tsror
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Image Data ◽  
Object Identification ◽  
Plant Diseases ◽  
Disease Classification ◽  
Successful Implementation ◽  
Potato Tubers ◽  
Data Set ◽  
Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

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