Applied AI researcher building crop-disease vision pipelines and no-finetune multilingual farm speech systems
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
—
works · OpenAlex
2
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—
The output axis is unscored: publication volume under this name cannot be reliably separated in OpenAlex, so total and per-year output are unavailable. This axis would measure cumulative publication counts and yearly production.
Impact—
Impact cannot be scored: the only raw signals are an h-index of 2 and an i10-index of 0, too sparse to judge citation influence on a possibly conflated record. This axis would measure citations and downstream uptake of the work.
Focus0/100
The focus score of 0 reflects a split record: reviewed 2026 work divides evenly (0.5/0.5) between agricultural computer vision and speech processing, while the OpenAlex topic profile points instead to computational drug discovery (6), acute myeloid leukemia (4) and cancer genomics (4). That mismatch suggests either a research pivot or author-name conflation, so no single research identity emerges from the data yet.
Collaboration48/100
A moderate collaboration score of 48 rests on 10 distinct co-authors and small recurring teams: the reviewed papers name Chandrashekar M S, Lakshmi Pedapudi, Aakash Singh, Vineet Singh and Sanyam Singh. The first-authored 2026 crop paper signals a lead role there, while the voice pipeline lists the researcher mid-author, consistent with contributing inside one compact group.
Activity32/100
Activity scores 32 on a sparse record: 2 works in the recent three-year window and OpenAlex yearly counts of 1 (2017), 2 (2018), 5 (2020), 2 (2021) and 2 (2026), with gaps in 2019 and 2022-2025. Both catalogued papers land in 2026, marking a concentrated recent burst rather than sustained annual output.
Reproducibility78/100
The strongest axis, scoring 78 as a mean across 2 reviewed papers. Evolve rates the no-finetune voice pipeline high for being modular and well-released, and the crop-disease pipeline medium because panel-made labels limit verification. Release practice and modular design drive the score, though a two-paper base is thin.
Research summary
Naga Ganesh is an early-stage applied AI researcher whose verifiable record currently rests on two 2026 arXiv preprints in agricultural AI, with no institution, country or ORCID attached; the profile is name-resolved from public OpenAlex bibliographic data. The first-authored paper rebuilds crop disease and pest diagnosis as a configurable multi-stage vision pipeline in which DaViT-Base outperforms Plantix and a fine-tuned vision-language model on production data, though panel-made labels cap the evidence. The second, mid-author paper contributes a model-agnostic, language-agnostic voice pipeline for noisy multilingual farm audio, cutting word error rate 16-23% on cloud ASR with most gains from speaker selection; Evolve rates it well-released and highly reproducible. Bibliometrics are thin: h-index 2, i10-index 0, ten distinct co-authors and a sparse yearly record. The focus score of 0 reflects a field distribution split evenly between agricultural computer vision and speech processing, alongside an OpenAlex topic profile dominated by computational drug discovery, acute myeloid leukemia and cancer genomics that does not match the reviewed AI work and hints at author-name conflation. Reproducibility is the strongest signal, averaging 78 across the two reviewed papers.
Collaboration pattern
Collaboration score 48 from 10 distinct co-authors. The reviewed work comes from a compact recurring team — Chandrashekar M S, Lakshmi Pedapudi, Aakash Singh, Vineet Singh and Sanyam Singh. On the 2026 crop paper Naga Ganesh is first author; on the voice pipeline the position is mid-list. The frequent-coauthor list duplicates names and includes the researcher's own entry, consistent with a small self-contained group.
Research keywords
Highlights
Configurable three-stage crop diagnosis pipeline
Label provenance caps the crop paper's evidence
No-finetune voice pipeline for noisy multilingual farm audio
Timeline
- 2017Earliest OpenAlex-recorded year under this name, with 1 work; the identity behind these records is not independently confirmed.
- 20182 works recorded by OpenAlex under this name.
- 2020Busiest recorded year with 5 works, though the topic mix behind these records diverges from the reviewed AI papers.
- 20212 works recorded; a publication gap then spans 2022-2025.
- 2026Returns with two arXiv preprints in agricultural AI: a first-authored crop-disease vision pipeline and a mid-author multilingual voice pipeline.
Papers in the Evolve catalog
Frequent co-authors
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.
- Bibliometrics are drawn from OpenAlex only; no ORCID, institution or country is available to confirm identity, so this profile is name-resolved from public bibliographic data for a common name.
- OpenAlex topics (computational drug discovery, acute myeloid leukemia, cancer genomics) diverge sharply from the reviewed agricultural AI papers; yearly counts and topics may conflate multiple authors sharing this name.
- Only 2 papers have been independently reviewed by Evolve; h-index 2 and i10-index 0 indicate an early-stage record, so the output and impact axes are left unscored.
- The reproducibility score of 78 is based on just 2 reviewed papers and should not be read as a stable pattern.