Plant Growth Stage Recognition
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About this skill
Problem to Solve
In crop management, a photo or short video often needs to be interpreted as a growth stage such as germination, seedling, vegetative growth, flowering, or fruiting. Manual assessment depends on experience and does not easily become a searchable, comparable record.
How the Skill Works
- Input: supports local image or video files, or network URLs; image formats include
jpg,jpeg, andpng, up to 20MB per file. - Analysis: uses computer vision and deep learning to detect the plant body, classify the growth stage, and summarize key developmental features into structured output.
- Result: provides recognition conclusions, risk notes, agronomic suggestions, and report links to support water and fertilizer management, pest control, and yield estimation.
- History lookup: reads historical reports from the cloud API with
--listand renders them as a Markdown table rather than relying on local memory or manual summaries.
Boundaries and Notes
- Source media should show a complete, well-lit plant; for field group shots, focus on one main plant.
- Results are reference information for agricultural decisions and do not replace professional guidance.
- Historical lists must be fetched from the cloud API; identity parameters are handled internally and should not be requested from users.
Use Cases
- Upload a single-plant photo during field inspection to classify germination, seedling, flowering, or fruiting.
- Run analysis on an orchard video to get current growth stage, risk notes, and water-fertilizer suggestions.
- Query the cloud report list when reviewing historical plant growth-stage recognition records.
- Turn structured recognition results and agronomic suggestions into crop growth ledger reference materials.
Best For
- Growing farmers who need field photos classified into growth stages.
- Agrotechnicians who need phenology evidence for water, fertilizer, and pest decisions.
- Agricultural data analysts who compare historical recognition reports over time.
- Integration engineers who add structured crop-stage outputs to smart farming workflows.
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