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Record W4407878568 · doi:10.1080/07060661.2025.2461015

Identification of fungi causing pre-harvest anthracnose and anthracnose-like symptoms on mango fruits ( <i>Mangifera indica</i> )

2025· article· en· W4407878568 on OpenAlexvenueno aff
Nidhi Kumari, Parul Shukla, Haripal Singh, Hari Shankar Singh, Sakshi Pandey, Alok Kumar Singh

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMangiferaBiologyFruit rotColletotrichum gloeosporioidesHorticultureIdentification (biology)Botany

Abstract

fetched live from OpenAlex

During field surveys in Uttar Pradesh, India between 2020–2022, we consistently encountered pre-harvest anthracnose and anthracnose-like symptoms on green mangoes, with incidence levels ranging from 20% to 40%, particularly during periods of heavy rainfall. This study aimed to identify the fungal pathogens associated with these symptoms using a polyphasic approach. Pathogenicity was confirmed on detached fruits of mango cv. Dashehari, with isolated pathogens including Colletotrichum asianum, C. siamense, Botryosphaeria dothidea, Diaporthe phoenicicola, Alternaria burnsii, Lasiodiplodia hormozganensis, and Parasympodiella eucalyptii. Colletotrichum asianum and C. siamense were identified as predominant through phylogenetic analysis of multiple genes (internal transcribed spacer [ITS], actin [ACT], chitin synthase [CHS-1], glyceraldehyde-3-phosphate dehydrogenase [GAPDH], and Mat1–2 gene region [APMAT] genomic regions). Lasiodiplodia hormozganensis and A. burnsii were identified through phylogenetic analysis based on ITS and translation elongation factor-1alpha (TEF) genes. Diaporthe phoenicicola was identified using ITS, TEF, and β-tubulin (TUB2)-based phylogenetic analysis. To the best of our knowledge, association of D. phoenicicola and A. burnsii with mango fruit spots is being reported for the first time in this study. Lasiodiplodia hormozganensis was the most aggressive pathogen, followed by C. siamense, C. asianum, B. dothidea, A. burnsii, D. phoenicicola, and P. eucalypti. Although the pathogenic behaviour of P. eucalyptii has been demonstrated on detached leaves and fruits, its role remains questionable due to its rare isolation. This study underscores the involvement of multiple Colletotrichum species and other fungal pathogens in mango anthracnose and highlights the need for further research into the diversity and geographical distribution of Colletotrichum species causing mango anthracnose in India.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.225
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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