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Precision Intelligence for the Soil.

Qidian VP is a concept-stage architecture for combining multimodal data processing with real-world grounding to test whether farmers can receive useful, safer first-line crop guidance.

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Advanced Agricultural AI
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Prototype View
Concept workflow preview

AI Architecture — Built on Google

Qidian VP is planned as a vertical AI application layered on top of Google's AI infrastructure. The MVP will test whether a focused Gemini workflow can support practical crop advisory use cases.

Built With Gemini 2.0 Flash (Multimodal) Vertex AI Cloud Speech-to-Text v2 Google Maps Platform Google Search Grounding Cloud Run · Firebase
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Gemini Vision — Crop Analysis

Gemini 2.0 Flash's native vision capability can process farm photos. Qidian VP plans to validate image-based crop issue detection across priority crops such as rice, maize, cassava, and sugarcane.

Gemini 2.0 Flash
240+ Target Conditions
mic

Voice — Cloud Speech-to-Text v2

The MVP will test farmer voice input, starting with Vietnamese farmer language. Broader regional dialect support is a roadmap item after initial validation.

Voice-First Roadmap
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Search Grounding — Reduced Hallucination Risk

The planned response flow will use Google Search Grounding to tie advice to selected agronomic references and reduce unsupported AI output.

Unified Gemini Response Engine

The intended MVP flow sends crop photo, symptom text or voice transcript, and selected context to Gemini. The target response is structured JSON with likely issue, confidence, suggested next step, estimated cost, and citations.

hub
database
Agri-Databases
Linked to global seed & soil banks
trending_up
Market Data
Candidate commodity price sources
thermostat
Local Weather
Candidate weather context
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Satellite Context
Future verification option

Grounded in Reality, Not Hallucinations.

Unlike a generic chatbot, Qidian VP is being designed to use Gemini's Search Grounding feature so responses can be traceable to selected agronomic references. This approach still needs to be tested with farmers and agronomists.

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    FAO & IRRI Open-Access Database Integration Candidate references include the International Rice Research Institute's disease library and FAO crop protection resources.
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    Commodity Price Feed Roadmap Regional commodity data sources will be evaluated after the diagnostic MVP is working.
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    Traceable Source Citations The planned output format includes source links so agronomists can review answer quality.

Data Sovereignty & Google Cloud Security

Qidian VP is intended to run on Google Cloud infrastructure. The MVP will be designed with consent, regional deployment choices, access controls, and encryption in mind before any real farmer data is collected.

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Encryption by Design
Planned use of Google Cloud encryption, access control, and key management options as the MVP matures.
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On-Device Inference Option
For later offline-first use cases, lightweight models may run on-device via Google AI Edge after the cloud workflow is validated.
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Privacy Compliance Roadmap
Designed with consent-first data collection and regional privacy requirements in mind; formal compliance work will follow real deployment planning.
Secure Cloud Infrastructure
MVP
Target Architecture
Cloud Run · Firebase · Gemini API
Technical Specifications

Target Benchmarks for MVP Validation

These are validation targets and cost assumptions, not measured production or pilot results.

speed

<10s

Response-Time Target

To be measured during MVP testing using crop image + symptom text/voice transcript + grounded context.

target

TBD

Diagnostic Accuracy

To be validated with agronomist review and documented test cases before any production claim.

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Est.

Cost per Diagnosis

A working cost model will be checked against actual Google Cloud usage during MVP testing.

monitoring Detailed Validation Metrics — Planned

Metric Value Notes
Gemini API Uptime To measure Track during MVP usage in selected Google Cloud regions
Voice Recognition Accuracy To measure Start with Vietnamese farmer language and expand only after validation
Offline Model Accuracy Future test AI Edge is a later offline inference direction, not part of the first MVP claim
Image Processing Low-latency Image pre-processing + upload for 12MP smartphone photo (varies by network conditions)
Concurrent Users Tested To test Load testing after MVP endpoint implementation
Cold Start Time To measure Cold starts and latency to be measured from real Cloud Run deployment
Monthly GCP Cost Estimate To be forecast from measured MVP usage, not from completed pilot data

Crop Disease Coverage Roadmap — 240+ Target Conditions

Qidian VP's target taxonomy covers major diseases, pests, and nutritional deficiencies affecting Southeast Asia's staple crops. Candidate references include public datasets and open agronomic resources; no proprietary pilot image dataset is claimed yet.

grass

Rice

98 target conditions

  • Rice Blast (Magnaporthe oryzae)
  • Bacterial Leaf Blight
  • Sheath Blight (Rhizoctonia)
  • Brown Planthopper (BPH)
  • Tungro Virus Complex
  • Stem Borer
  • Nitrogen/Phosphorus Deficiency
  • + 91 more target conditions
spa

Maize

62 target conditions

  • Fall Armyworm (Spodoptera)
  • Northern Leaf Blight
  • Stalk Rot Complex
  • Downy Mildew
  • Maize Streak Virus
  • Ear Rot (Fusarium)
  • Zinc Deficiency
  • + 55 more target conditions
eco

Cassava

47 target conditions

  • Cassava Mosaic Disease
  • Bacterial Blight (Xanthomonas)
  • Cassava Mealybug
  • Brown Leaf Spot
  • Anthracnose
  • White Fly Infestation
  • Root Rot
  • + 40 more target conditions
yard

Sugarcane

33 target conditions

  • Red Rot (Colletotrichum)
  • Smut Disease
  • Top Borer
  • Rust (Puccinia)
  • Wilt Disease
  • Leaf Scald
  • Iron Chlorosis
  • + 26 more target conditions
update

Expansion Roadmap:

Coffee, rubber, and tropical fruit crops are future roadmap candidates. University or research collaboration will be pursued only after the initial MVP scope is validated.

System Architecture

Planned end-to-end data flow from farmer's smartphone to AI-assisted draft guidance and back — all designed for Google Cloud.

smartphone
Client Layer

Flutter Mobile App

PWA (Lite version)

Google AI Edge SDK

Firebase Auth

api
API Gateway

Cloud Run (Serverless)

Cloud Endpoints

Cloud Armor (DDoS)

Identity Platform

psychology
AI Engine

Gemini 2.0 Flash

Vertex AI Pipeline

Search Grounding API

Speech-to-Text v2

storage
Data Storage

Firestore · Cloud Storage · BigQuery

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External APIs

Google Maps · Weather API · Commodity Exchanges

monitoring
Observability

Cloud Monitoring · Cloud Trace · Error Reporting

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Limitations & Known Constraints

What Qidian VP cannot do yet — and what we're working to improve.

  • warning Qidian VP does not replace professional agronomist consultation for complex multi-disease interactions.
  • warning Accuracy has not yet been validated; crop-by-crop performance must be measured during pilot testing.
  • warning Voice recognition must be tested in noisy field environments before support claims are made.
  • warning Offline capture is planned; on-device diagnosis and full treatment workflows require further validation.
  • warning Initial scope targets 4 crop categories; tropical fruits and tree crops remain future candidates.

Review the MVP Architecture

We are seeking technical feedback, cloud support, and early validation partners before building the first field-testable MVP.

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