Chandrashekar M S

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Unclaimed · AI-structured profileOpenAlex

Applied AI researcher building agricultural computer vision and multilingual speech pipelines for the farming domain

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-15) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.

Research profile

Output—

Output cannot be scored yet: the OpenAlex snapshot carries no complete publication-history data for this author. This axis would measure total works, authorship positions, and yearly output volume to show how productivity trends over time; on current evidence no estimate is made.

Impact—

Impact has no score yet because citation data is unavailable; the recorded h-index and i10-index are both 0, consistent with a newly opened profile. This axis would measure citations, citing works, and venue visibility once citations accrue.

Focus0/100

The focus score is 0, reflecting an even split in the field distribution: 50% applied computer vision for agriculture and 50% speech processing (ASR). Topic data reinforces the breadth — five topics (advanced neural network applications, speech recognition and synthesis, smart agriculture and AI, speech and audio processing, speech and dialogue systems) each appear once, so no single specialization has consolidated across the two catalogued works.

Collaboration48/100

Collaboration scores 48 from the underlying bibliometric data: 10 distinct coauthors appear across the author's recent works, with Naga Ganesh, Lakshmi Pedapudi, Aakash Singh, Vineet Singh, and Sanyam Singh recurring. Both catalogued 2026 papers place the author in a middle position without corresponding-author status, indicating steady team participation rather than independent research leadership so far.

Activity32/100

Activity scores 32. The snapshot records 2 works in the last three years, and the by-year distribution shows both appearing in 2026 with no earlier entries. Both outputs are arXiv preprints, so the low score reflects a short visible publication history in the index rather than any documented slowdown.

Reproducibility78/100

Reproducibility is the strongest axis at 78, averaged over 2 papers assessed by Evolve. The agricultural voice pipeline is rated high readiness and described as well-released and credible; the crop-disease vision pipeline is rated medium, with panel-made labels capping the evidence. A release-oriented working habit is visible, though the assessed sample is only two papers.

Research summary

Chandrashekar M S is an early-stage applied AI researcher whose visible work sits at the intersection of agriculture and machine learning. The OpenAlex snapshot records 2 works in the last three years, both appearing in 2026, and the Evolve catalog carries two 2026 arXiv preprints: a configurable multi-stage vision pipeline for crop disease and pest diagnosis, and a model-agnostic, language-agnostic voice pipeline improvement for agricultural ASR. The field distribution is an even 50/50 split between applied computer vision for agriculture and speech processing, and five topics — advanced neural network applications, speech recognition and synthesis, smart agriculture and AI, speech and audio processing, and speech and dialogue systems — each appear once, yielding a focus score of 0. Both papers list the author in a middle position without corresponding status, within teams of five recurring collaborators drawn from 10 distinct coauthors. Reproducibility is the profile's strongest signal at 78 across 2 assessed papers: the voice pipeline is rated high readiness and described as well-released, while the vision study is medium, with panel-made labels capping the evidence. The citation footprint is not yet established — the recorded h-index and i10-index are 0, and the output and impact dimensions are unscored for lack of data. On current evidence, this is a contributor profile built on practical, release-oriented engineering in agri-tech AI.

Collaboration pattern

The author works in small applied-AI teams: 10 distinct coauthors appear across recent works, with Naga Ganesh, Lakshmi Pedapudi, Aakash Singh, Vineet Singh, and Sanyam Singh recurring. Both Evolve-catalogued 2026 papers place the author in a middle position without corresponding status, suggesting a contributor role on engineering-heavy, agriculture-domain projects rather than sole leadership.

Research keywords

crop disease detectionpest classificationvision-language modelsautomatic speech recognitionspeaker diarizationlow-resource languagesIndic languagesspeech enhancementagricultural AIspeech and dialogue systemssmart agriculture

Highlights

Modular no-finetune voice pipeline cuts farm-audio error rates
Evolve-reviewed work on a model-agnostic, language-agnostic ASR pipeline for noisy multilingual agricultural audio reports a 16-23% word-error-rate reduction in cloud settings, with most of the gain coming from speaker selection; it is rated high reproducibility readiness and described as well-released, credible work.
Three-stage configurable vision pipeline for crop diagnosis
This production-data study rebuilds crop disease and pest diagnosis as three configurable stages; DaViT-Base outperforms Plantix and a fine-tuned vision-language model (Qwen3-VL), though panel-made labels cap the evidence. Evolve rates its reproducibility readiness as medium.

Timeline

2026Co-authored 'Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain' on arXiv, reporting a 16-23% word-error-rate reduction in cloud settings for noisy multilingual farm audio.
2026Co-authored 'Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis' on arXiv, in which DaViT-Base outperformed Plantix and a fine-tuned vision-language model.
2026Both papers entered the Evolve review catalog with reproducibility readiness rated high (voice pipeline) and medium (vision pipeline), anchoring this early-stage profile.

Papers in the Evolve catalog

Frequent co-authors

Naga Ganesh×2 Lakshmi Pedapudi×2 Naga Ganesh×1 Chandrashekar M S×1 Lakshmi Pedapudi×1 Aakash Singh×1 Vineet Singh×1 Aakash Singh×1 Sanyam Singh×1 Vineet Singh×1

About this profile

This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.

Bibliometric metrics (h-index, i10-index, coauthor counts, by-year works) come from an OpenAlex snapshot only; no institution, country, or ORCID is attached to this profile.
'Chandrashekar M S' is name-resolved from public bibliographic data; without an ORCID, disambiguation among similarly named authors is uncertain.
Output and impact axes are unscored because citation and full publication-history data are insufficient, with h-index and i10-index recorded as 0.
Only 2 Evolve-reviewed arXiv papers underpin the reproducibility score and highlights; other venues or non-arXiv work may be missing.