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Plant Growth Stage Recognition

Professional Updated 2026.08.29

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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, and png, 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 --list and 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.