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Indoor Plant Light Stress Detection

Life Service Updated 2026.08.30

Paste the following prompt into your AI chat to install this skill:

Please follow https://skillhub.cn/install/skillhub.md and install @user_bb47e3e3/indoor-plant-light-stress-detect-analysis.

About this skill

Problem

Indoor plant issues are often light-related rather than disease-related: weak light can cause elongated internodes, thin or pale leaves, sparse growth, and leaning toward light; too much direct light can cause scorched edges, brown necrotic spots, curling, and bleaching. Visual checks are easy to misread in homes, offices, and smart planter setups.

How it works

The skill accepts local image or video paths and network URLs, supporting jpg, png, mp4, avi, and mov files up to 10MB. It analyzes whole-plant structure and leaf details for low-light stress or strong-light damage, and can combine optional lux sensor readings to classify the condition as insufficient, normal, or excessive. It returns a structured report with detected signs, risk notes, care suggestions, and a report link; historical reports should be fetched through the cloud API rather than local memory.

Boundaries

Use the output as a care reference, not as equipment selection, grow-light model advice, or professional agronomic diagnosis. Capture both whole-plant and close-up leaf frames under stable lighting, and avoid overexposure or darkness. The skill should not recommend specific device parameters; it should give actionable directions such as moving the plant toward a window, adding shade, or adjusting supplemental light duration.

Use Cases

  • A home gardener sees elongated internodes and pale leaves on a pothos, then uploads whole-plant and leaf photos to decide whether to move it near a window.
  • An office plant manager checks desk plants for scorched edges and curling, using fixed-camera images to decide whether to block direct sunlight.
  • A smart planter integration engineer connects image and lux data to verify stable output for insufficient, normal, and excessive light categories.
  • A remote plant-care consultant receives a client video and reviews cloud-hosted historical light reports to check whether shading worked.

Best For

  • Home plant caretakers who want photo-based guidance on increasing or reducing light exposure.
  • Office facilities or admin staff who inspect workplace plants and record light adjustment recommendations.
  • Smart-planter integration engineers who need image, video, and lux data turned into structured diagnosis results.
  • Remote plant-care consultants who review cloud historical reports to track client plant conditions.