Agri AI that speaks the farmer's language — literally.
Vernacular agri-advisory speech, crop & pest imagery and farmer-query data — collected in the field for agritech AI that reaches real farmers.
Advisory bots and crop-diagnosis models are useless if they only work in English text. We collect and annotate vernacular agri-advisory speech, crop and pest imagery, and real farmer queries across India's languages and dialects — so agritech AI actually reaches the field.
What teams in Agriculture are up against
Agriculture is where AI meets the hardest data reality in India:
- Farmers speak, they don't type. Advisory has to work in vernacular speech, full of local crop names, units and dialect — not English text.
- Imagery is local and long-tail. Crop, pest and disease imagery varies by region and season; generic vision datasets don't cover Indian fields.
- Queries are code-mixed and contextual. Real farmer questions blend languages and assume local context models have never seen.
- Connectivity and literacy constrain UX. Voice-first, low-bandwidth interaction demands robust speech and intent data.
Compliance & residency
Field data collection in rural India carries consent and ethics obligations:
- DPDP Act, 2023 — informed consent in the respondent's language; voices and faces de-identified where needed.
- Fair field practice — documented, fairly-compensated field operations with provenance per record.
- Provenance & licensing — every clip and image tagged with region, crop, consent and usage license.
How we solve it
Field-collected agri data, region by region
Vernacular speech, local imagery and real farmer queries — captured and labeled where the crops actually grow.
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Vernacular agri-speech collection
Consented advisory and query speech across languages and dialects, with local crop names and units — captured in the field.
Multilingual data collection -
Crop & pest image annotation
Bounding boxes, segmentation and disease/pest labeling on region-specific crop imagery for diagnosis and advisory models.
Data annotation -
Advisory evaluation sets
Evaluation data that checks whether your model gives correct, locally-appropriate advice across languages and crops.
Cultural & cross-lingual evaluation
Proof
Why teams trust us with this vertical
- first capture for low-literacy, low-bandwidth UX
- Voice
- languages & dialects across agri regions
- Multi
- consented, fairly-compensated rural collection
- Field
first capture for low-literacy, low-bandwidth UX
languages & dialects across agri regions
consented, fairly-compensated rural collection
Go deeper
Datasets and services for this vertical
Jump straight into the catalog filtered for this domain, or scope a custom program.
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Browse the dataset catalog
See rights-cleared, documented datasets filtered to this vertical — or commission a custom set.
View datasets -
Explore our services
End-to-end collection, annotation, RLHF/DPO, evaluation and safety — applied to your use case.
All services
FAQ
Agriculture — common questions
- Can you collect agri data in specific languages and regions?
Yes. We run consented field operations targeted to the languages, dialects, crops and regions you serve. See Multilingual Data Collection and agriculture datasets.
- Do you annotate crop and pest imagery?
Yes — bounding boxes, segmentation and disease/pest labels on region-specific imagery. See Data Annotation.
- How is rural consent handled?
Informed consent in the respondent's language, fair compensation and DPDP-aligned de-identification, with provenance tagged per record.
Build an AI data program for Agriculture.
Tell us your languages, modalities and use case — we'll scope a rights-cleared, documented data program and a delivery schedule.
Where our data services apply
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Multilingual Data Collection
Native-speaker audio, video, image and text collection across Indic, African and low-resource languages — field-grade, consented, documented.
Explore the service -
Data Annotation
Speech, NLP, CV and multimodal annotation at IAA ≥ 0.85 with two-pass QA — built for foundation-model SLAs.
Explore the service -
Cultural & Cross-Lingual Evaluation
Evaluation for honorifics, code-mix, idioms, caste-safety and pragmatic correctness — beyond translated MMLU.
Explore the service