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BhashaBench-Krishi (BBK): Benchmarking AI on Indian Agricultural Knowledge

Overview

BhashaBench-Krishi (BBK) is the first large-scale, authentic benchmark designed to rigorously evaluate AI models on Indian agricultural knowledge. Tailored for India’s diverse agro-ecological zones, crops, languages, and farming practices, BBK draws from 55+ official government agricultural exams to assess models' ability to provide precise, region-aware, policy-relevant, and actionable agricultural advice.

Key Features

• Languages: English and Hindi (with plans for more Indic languages)
• Exams: 55+ unique agricultural government and institutional exams across India
• Domains: 25+ agricultural and allied science domains, spanning over 270 topics
• Questions: 15,405 rigorously validated, exam-based questions
• Difficulty Levels: Easy (6,754), Medium (6,941), Hard (1,710)
• Question Types: Multiple Choice, Assertion-Reasoning, Match the Column, Rearrange the Sequence, Fill in the Blanks
• Focus: Practical, context-rich, region-specific agricultural knowledge essential for Indian farmers

Dataset Statistics

Metric	Count
Total Questions	15,405
English Questions	12,648
Hindi Questions	2,757
Subject Domains	25+
Government Exams Covered	55+

Dataset Structure

Test Set

The test set consists of the BhashaBench-Krishi (BBK) benchmark, which contains approximately 15,405 multiple-choice questions across 2 Indic languages (English and Hindi).
We will add support for more Indic languages in upcoming versions.

Subjects spanning BBK

Subject Domain	Count
Agri-Environmental & Allied Disciplines	176
Agricultural Biotechnology	524
Agricultural Chemistry & Biochemistry	281
Agricultural Economics & Policy	627
Agricultural Engineering & Technology	244
Agricultural Extension Education	774
Agricultural Microbiology	111
Agriculture Communication	254
Agriculture Information Technology	190
Agronomy	5078
Animal Sciences	148
Crop Sciences	549
Dairy & Poultry Science	89
Entomology	696
Fisheries and Aquaculture	34
General Knowledge & Reasoning	661
Genetics and Plant Breeding	389
Horticulture	2070
Natural Resource Management	193
Nematology	184
Plant Pathology	397
Plant Sciences & Physiology	129
Seed Science and Technology	202
Soil Science	1357
Veterinary Sciences	48

Usage

Since this is a gated dataset, after your request for accessing the dataset is accepted, you can set your HuggingFace token:

export HF_TOKEN=YOUR_TOKEN_HERE

To load the BBK dataset for a Language:

from datasets import load_dataset
language = 'Hindi'
# Use 'test' split for evaluation
split = 'test'
language_data = load_dataset("bharatgenai/BhashaBench-Krishi", data_dir=language, split=split, token=True)
print(language_data[0])

Evaluation Results Summary

• 29+ models evaluated, including GPT-4o, Qwen3-235B, and various open-source LLMs.

• Top accuracy:

  • English: 70%+ by best models
  • Hindi: 60–65%, indicating room for improvement

• Strong domains:

  • Agricultural Biotechnology, Plant Sciences, Veterinary Sciences (~80% accuracy)

• Weak domains:

  • Agri-Environmental Sciences, Nematology, Regional Crop Management (<50%)

• Challenges:

  • Hard questions and non-MCQ formats remain challenging across models

For detailed results and analysis, please refer to our blog.

Citation

Please cite our benchmark if used in your work:

@misc{bhashabench-krishi-2025,
  title  = {BhashaBench-Krishi: Benchmarking AI on Indian Agricultural Knowledge},
  author = {BharatGen Research Team},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/bharatgenai/bhashabench-krishi}},
  note   = {Accessed: YYYY-MM-DD}
}

License

This dataset is released under the CC BY 4.0.

Contact

For any questions or feedback, please contact:

• Vijay Devane ([email protected])
• Mohd. Nauman ([email protected])
• Bhargav Patel ([email protected])
• Kundeshwar Pundalik ([email protected])

Links

• GitHub Repository 💻
• Paper 📄