Phylogenetic network analysis of South and North American Corynespora cassiicola isolates from tomato, cucumber, and novel hosts

Aguiar, F. M.; Vallad, Gary E.; Timilsina, Sujan; Veloso, Josiene Silva; Fonseca, M. E. N.; Boiteux, L. S.; Reis, Ailton

doi:10.1007/s10658-022-02505-x

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…The necrotrophic fungus C. cassiicola causes disease across a wide range of hosts [117]. Corynespora leaf disease of rubber tree causes severe yield loss of rubber in African and Asian plantations; however, its influence is negligible in South America [74,118].…”

Section: France Malaysia and Thailandmentioning

confidence: 99%

The Diseases and Pests of Rubber Tree and Their Natural Control Potential: A Bibliometric Analysis

Chen

et al. 2023

Agronomy

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In order to trace the research history of diseases and pests in rubber tree and explore the potential for their natural control, a bibliometric analysis was conducted based on relevant documents retrieved from the Clarivate Analytics Web of Science (WoS) core collection SCI-E database. VOSviewer software was utilized to analyze the research distribution, scientific collaboration, knowledge structure, and research frontiers. The results show that annual publications on the diseases and pests of rubber tree have increased rapidly after 2005 after a long period of emergence and fluctuation. A total of 624 relevant publications from 51 countries/regions were identified. China was the most productive country with 152 documents, most of which were related to Colletotrichum leaf disease, powdery mildew, and other emerging diseases of rubber tree. France and Brazil produced rich research to tackle South American leaf blight, and have established a close collaborative relationship. Based on the analysis of themes and trend topics, pathogenicity mechanisms of fungal pathogens and plant defense mechanisms are currently hot topics. By further looking into the research, the defense-related genes of rubber tree and antagonistic mechanisms behind candidate biocontrol agents reveal great potential in developing natural control strategies. This study provides a useful reference about the progress and evolution of research into diseases and pests in rubber tree.

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Section: France Malaysia and Thailandmentioning

confidence: 99%

The Diseases and Pests of Rubber Tree and Their Natural Control Potential: A Bibliometric Analysis

Chen

et al. 2023

Agronomy

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“…Among the factors that increase its importance, its ability to survive in crop residues has a major impact [5]. Another aspect of great importance for this disease is its host range, of which 530 plant species have been reported [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

de Queiroz Otone,

Theodoro,

Santana

et al. 2024

AgriEngineering

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Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

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Biocontrol of Corynespora cassiicola in soybean using a new phenethyl alcohol-producing Meyerozyma caribbica strain

et al. 2023

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Phylogenetic network analysis of South and North American Corynespora cassiicola isolates from tomato, cucumber, and novel hosts

Cited by 3 publications

References 39 publications

The Diseases and Pests of Rubber Tree and Their Natural Control Potential: A Bibliometric Analysis

The Diseases and Pests of Rubber Tree and Their Natural Control Potential: A Bibliometric Analysis

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Biocontrol of Corynespora cassiicola in soybean using a new phenethyl alcohol-producing Meyerozyma caribbica strain

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