AI solutions · University–industry partnership
AI for Agriculture
Joint validation trials of AI and LLMs in agriculture with Guizhou University. We test models in real field conditions to shorten the path from lab to farm.
Focus areas of the validation trials
The partners jointly validate LLM inference, fine-tuning and applications in agricultural scenarios. The university contributes subject-area scenarios, data and evaluation methods; HyperSource contributes compute, model engineering and production deployment. Together they form a smart-agriculture trial framework that is verifiable, reproducible and ready to scale.
Not just another demo
The goal is a repeatable process for validating AI in agriculture: how data is collected, how models are evaluated, how results are accepted and how capabilities go live. Every step can be reproduced.
Experiments first, driven by evidence
Define task metrics and evaluation sets first, then run model comparisons and human spot checks. This avoids models that demo well but fail in the field.
Grounded in academic research
Drawing on the university's agricultural disciplines and research settings, we bring real problems in crop farming, grassland livestock and extension services into the validation loop.
Engineered to replicate
Validated capabilities can be packaged as APIs, knowledge base assistants or private services, then rolled out to farms, cooperatives and government extension networks.
From field sensing to farming decisions
Capability modules built through the validation trials can be combined to fit each scenario.
Crop monitoring and alerts
Combines weather, remote sensing and IoT data to continuously track soil moisture, crop growth and disaster risk, and uses LLMs to write clear alerts and recommended actions.
- Growth assessment and yield estimate support
- Risk alerts for drought, frost, flooding and more
- Unified multi-source data intake and operations dashboard
- Alerts can be pushed via SMS / WeChat mini program
Pest and disease identification
Vision models identify pests and diseases in images of leaves, fruit and fields. Results are checked against expert review to keep improving accuracy.
- Identify from a phone photo
- Regional outbreak trend statistics
- Treatment and agronomic guidance (with optional human confirmation)
- On-premises private deployment and offline packages
Farming knowledge Q&A
Turns growing manuals, local standards, research findings and expert experience into a knowledge base, with a focus on source citations, hallucination control and understanding dialects and colloquial speech.
- Handles dialect and colloquial questions
- Answers cite their sources
- Connects to WeChat / mini programs / farming hotlines
- Private regional knowledge bases
Production planning and decision support
Recommends irrigation and fertilization, machinery use and harvest timing, and turns strategies proven in trials into decision assistants you can call on.
- Irrigation and fertilization plans and field task suggestions
- Farm machinery scheduling support
- API integration with business systems / IoT platforms
- Configurable human approval for key recommendations
Who can use the results
Universities and research teams
Need stable compute, reproducible experiment environments and engineering-grade evaluation tools to turn research results into capability packages that can be demonstrated and delivered.
Agribusinesses / cooperatives
Large crop and livestock operations need unified monitoring, standard procedures and risk alerts. Start with a small pilot, then scale.
Extension services and government platforms
Build regional platforms for farming Q&A, pest and disease monitoring and industry services, with auditability, private deployment and local service.
Agtech software vendors
Embed LLM and vision capabilities into existing crop monitoring, machinery, traceability and e-commerce systems, without the long cycle of building models in-house.
From scenario definition to field acceptance
The partnership follows four steps, so every capability has controlled experiments and a path to deployment.
- 01
Scenario: define tasks and metrics
Specify the crop / livestock, business goals and acceptance metrics such as accuracy, recall, response time and human review cost.
- 02
Data: corpus and sample factory
Organize field images, farming manuals and Q&A logs, then clean, label, de-identify and version them.
- 03
Model: select · fine-tune · compare
Compare general models, domain fine-tuned models and rule-based baselines on a shared evaluation set to reach explainable conclusions.
- 04
Deployment: pilot and productize
Small pilot → human review → delivery via API / WeChat mini program / private deployment, with hard cases continuously fed back.
Compute and model foundation for the trials
Integrated compute, models and data engineering for joint trials and later commercialization.
GPU Compute Rental · GPU servers
Compute for fine-tuning and inference that scales with each experiment, so universities and agribusinesses avoid the cost of building their own clusters.
Explore GPU rentalLLM Fine-Tuning
Cleaning and labeling of agricultural corpora plus LoRA / QLoRA fine-tuning, turning validation-set iterations into lasting model assets.
See fine-tuningTokenHub – Unified LLM Gateway
Validated capabilities connect quickly to crop monitoring, machinery and traceability systems through an OpenAI-compatible API.
Explore TokenHubAI Compute Appliance
On-premises platform for farms, cooperatives and extension stations, with offline operation.
Explore the applianceFAQ
We don't have data ready. Can we still start?
Yes. Data is the first step of every trial: we organize, label and de-identify field images, farming manuals and past Q&A logs, and build the evaluation set at the same time.
How accurate are identification and Q&A?
Accuracy is determined by controlled experiments on a shared evaluation set. Results vary widely by crop, disease and image conditions, so we don't commit to figures before a pilot.
Can it work offline in fields without internet?
Pest and disease ID and farming Q&A support on-premises deployment and offline packages. They can run on an AI Compute Appliance at a farm or extension station.
How can a university research group take part?
Bring your research scenarios, data and evaluation methods. HyperSource provides the compute, model engineering and production deployment. A project can start with a single crop or a single problem.
Other AI solutions
Turn validation trials into products that work in the field
Whether you are a university research group, an agribusiness or an extension platform builder, you can use this joint validation approach to define your scenario and launch a pilot quickly.
- leo@hs.com.cn
- 24/7 technical support