Preparing Plant Data via AI Prompt¶
Kamerplanter manages 80+ structured fields per plant species -- from taxonomy and nutrient profiles to pest data and companion planting relationships. Manually collecting this data from various sources is time-consuming. That's why we use Claude Code Agents to fully prepare and quality-check new plants.
Overview: The AI Pipeline¶
flowchart LR
A["Plant name\n(e.g. 'Basil')"] --> B["plant-info-document-generator"]
B --> C["Plant document\nspec/ref/plant-info/*.md"]
C --> D["agrobiology-requirements-reviewer"]
D --> E["Review report\nspec/requirements-analysis/"]
E -->|Corrections| C
C --> F["Seed data / CSV import\n(REQ-012)"] The workflow consists of three steps:
- Generation -- An AI agent researches and creates the plant document
- Review -- A second agent checks the data for scientific accuracy
- Import -- The reviewed document serves as the basis for data import
Step 1: Generate Plant Document¶
The plant-info-document-generator agent automatically researches all relevant data and creates a structured Markdown document under spec/ref/plant-info/.
Invocation in Claude Code¶
or for multiple plants at once:
Claude Code recognizes the context and automatically activates the plant-info-document-generator agent.
What the Agent Does¶
- Analyze input -- Identifies the scientific name, family, and genus
- Research -- Searches the web for:
- Taxonomy and master data (GBIF, RHS, USDA)
- Growth phases with PPFD, VPD, temperature per phase
- Nutrient profiles (NPK, EC, pH per phase)
- Pests and diseases with beneficial organisms
- Care and overwintering instructions
- Crop rotation and companion planting partners
- Create document -- Writes a complete document with all Kamerplanter field references
Result¶
The document is saved as:
Example: spec/ref/plant-info/ocimum_basilicum.md
Document Structure¶
Each generated document contains these sections:
| Section | Content | Kamerplanter Reference |
|---|---|---|
| 1. Taxonomy & Master Data | Botanical classification, sowing/harvest times, propagation, toxicity | REQ-001 Species/Cultivar |
| 2. Growth Phases | Phase overview, requirement profiles, nutrient profiles, transition rules | REQ-003 Phase Control |
| 3. Fertilization | Recommended products (mineral + organic), feeding schedule, mixing order | REQ-004 Nutrient Logic |
| 4. Care Instructions | Care profile, annual calendar, overwintering | REQ-022 Care Reminders |
| 5. Pests & Diseases | Pests, diseases, beneficial organisms, treatment methods | REQ-010 IPM System |
| 6. Crop Rotation & Companion Planting | Good/bad neighbors, crop rotation classification | REQ-013 Planting Runs |
| 7. Similar Species | Alternatives and related species | -- |
| 8. CSV Import Data | Ready-made CSV lines for REQ-012 import | REQ-012 Master Data Import |
Each table includes a KA-Field column referencing the exact Kamerplanter database field.
Step 2: Scientific Review¶
The agrobiology-requirements-reviewer agent checks the document from an agrobiology expert's perspective.
Invocation in Claude Code¶
What the Review Agent Checks¶
- Taxonomy -- Scientific names per APG IV, correct family assignment
- Light data -- PPFD/DLI instead of Lux, photoperiodism correct
- Climate data -- VPD calculation plausible, day/night temperature separated
- Nutrients -- EC ranges realistic, mixing order correct (CalMag before sulfates)
- Pests -- Scientific names, IPM tier approach (prevention > monitoring > intervention)
- Toxicity -- ASPCA data for cats/dogs verified
- Companion planting -- Compatibilities biologically justified
Result¶
The review report is saved under:
Findings are classified as:
| Category | Meaning |
|---|---|
| Immediate correction needed | |
| Important aspects missing | |
| Needs precision | |
| Best practice recommendation |
Step 3: Import into Kamerplanter¶
The reviewed documents serve as the basis for data import:
Option A: Seed Data (Developer)¶
Plant data is built into a Python seed script at src/backend/app/migrations/seed_plant_info.py. The seed reads the Markdown documents and creates the corresponding ArangoDB documents.
Option B: CSV Import (End User)¶
Each plant document contains ready-made CSV lines in section 8 that can be uploaded via the REQ-012 import function.
Existing Plant Documents¶
Currently 32 plants are fully documented:
Including vegetables (tomato, pepper, cucumber, zucchini, ...), herbs (basil, parsley, dill, chives, ...), ornamentals (dahlia, petunia, sunflower, ...), and houseplants (monstera, peace lily, spider plant, guzmania).
Tips for Best Results¶
Provide specific cultivar names
Instead of "tomato", try "tomato San Marzano" -- the agent can then research cultivar-specific data (maturity time, resistances, growth type) more accurately.
Specify the growing context
"Basil for indoor growing in a grow tent" yields different results than "basil for the garden" -- especially for light, temperature, and fertilization data.
Use batch processing
Request multiple related plants at once (e.g., all kitchen herbs) -- the agent can then document companion planting relationships between them right away.
Always run the review
AI-generated data can contain errors. The agrobiology-requirements-reviewer typically finds 2--5 corrections per document. EC values, toxicity data, and pest scientific names deserve particular scrutiny.
Involved Claude Code Agents¶
| Agent | File | Task |
|---|---|---|
plant-info-document-generator | .claude/agents/plant-info-document-generator.md | Researches and creates plant documents |
agrobiology-requirements-reviewer | .claude/agents/agrobiology-requirements-reviewer.md | Scientific review (botany, crop science, IPM) |