What makes Leaf Aging Fall Prediction | 植物叶片老化/脱落预测 notable?

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Leaf Aging Fall Prediction | 植物叶片老化/脱落预测

Using a fixed indoor camera to continuously capture leaf images of houseplants from the same angle every day, AI vision techniques detect leaf color changes (green → yellow → brown), loss of glossiness (reduced surface reflectance), and formation of the abscission zone at the petiole base (angle change). | 通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析技术检测叶片颜色变化（从绿到黄再到褐）、光泽度下降（叶面反光减弱）、叶柄基部离层形成（角度变化）等老化进程，并基于历史图像序列的时间序列模型预测未来3-7天内叶片脱落的风险时段。系统每日生成老化报告，当预测即将落叶时推送提醒（如'富贵竹下位叶预计3天后脱落，可提前剪除以保持美观'）。 Skill: Leaf Aging Fall Prediction | 植物叶片老化/脱落预测 Owner: 18072937735 Summary: Using a fixed indoor camera to continuously capture leaf images of houseplants from the same angle every day, AI vision techniques detect leaf color changes (green → yellow → brown), loss of glossiness (reduced surface reflectance), and formation of the abscission zone at the petiole base (angle change). | 通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析

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Rank

Safety

Downloads

1.8k

Updated

Oct 10, 2026

Version

1.0.16

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.8K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.8K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.16release · observed Oct 3, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-leaf-aging-fall-prediction-analysis

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-18072937735-smyx-leaf-aging-fall-prediction-analysis/snapshot"

Documentation

CLAWHUB

141,078 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "smyx-leaf-aging-fall-prediction-analysis"
description: "Using a fixed indoor camera to continuously capture leaf images of houseplants from the same angle every day, AI vision techniques detect leaf color changes (green → yellow → brown), loss of glossiness (reduced surface reflectance), and formation of the abscission zone at the petiole base (angle change). | 通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析技术检测叶片颜色变化（从绿到黄再到褐）、光泽度下降（叶面反光减弱）、叶柄基部离层形成（角度变化）等老化进程，并基于历史图像序列的时间序列模型预测未来3-7天内叶片脱落的风险时段。系统每日生成老化报告，当预测即将落叶时推送提醒（如'富贵竹下位叶预计3天后脱落，可提前剪除以保持美观'）。"
version: "1.0.16"
license: "MIT-0"
---

# 🍂 Leaf Aging Fall Prediction | 室内绿植叶片老化/脱落预测
> **智能分析中枢** · 图片/视频智能分析 · 结构化报告 · 历史报告云端查询

---

## 🧭 技能概览 | Overview

| 模块 | 内容 |
|---|---|
| 🏷️ 技能名称 | **植物叶片老化/脱落预测** |
| 🎯 核心目标 | 通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析技术检测叶片颜色变化（从绿到黄再到褐）、光泽度下降（叶面反光减弱）、叶柄基部离层形成（角度变化）等老化进程，并基于历史图像序列的时间序列模型预测未来3-7天内叶片脱落的风险时段。系统每日生成老化报告，当预测即将落叶时推送提醒（如'富贵竹下位叶预计3天后脱落，可提前剪除以保持美观'）。 |
| 🖼️ 输入类型 | 图片、视频、本地文件、网络 URL |
| 📝 输出能力 | 结构化分析报告、识别/监测结果、建议与报告链接 |
| 🧩 场景码 | `SMYX_LEAF_AGING_FALL_PREDICTION_ANALYSIS` |

Using a fixed indoor camera to continuously capture leaf images of houseplants from the same angle every day, AI vision techniques detect leaf color changes (green → yellow → brown), loss of glossiness (reduced surface reflectance), and formation of the abscission zone at the petiole base (angle change). Based on a time-series model over historical images, the skill predicts the risk window for leaf fall within the next 3-7 days. It helps users distinguish natural turnover from stress-induced leaf drop and adjust care in advance (raise humidity, fertilize, prune, etc.). Application scenarios: indoor potted plant care, plant rental companies, botanical garden greenhouses. The system generates a daily aging report and pushes alerts when leaf fall is imminent (e.g., 'lower leaves of lucky bamboo expected to fall in 3 days, prune in advance to keep it tidy'). Skill features: leaf aging and shedding are normal life-cycle events, but early shedding often signals environmental issues. AI fall-time prediction lets users clean up dead leaves proactively, keep plants tidy, and tune care based on predicted speed (e.g., higher humidity slows senescence). Can be integrated into smart planters or gardening apps for refined care recommendations.

通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析技术检测叶片颜色变化（从绿到黄再到褐）、光泽度下降（叶面反光减弱）、叶柄基部离层形成（角度变化）等老化进程，并基于历史图像序列的时间序列模型预测未来3-7天内叶片脱落的风险时段。该技能帮助用户提前了解植物自然更新或胁迫落叶，及时调整养护（如增加湿度、施肥、修剪等）。应用场景：室内盆栽养护、绿植租赁公司、植物园温室。系统每日生成老化报告，当预测即将落叶时推送提醒（如'富贵竹下位叶预计3天后脱落，可提前剪除以保持美观'）。技能特点：叶片老化脱落是植物正常生命周期的一部分，但过早脱落可能提示环境问题。通过AI预测脱落时间，用户可提前清理枯叶，保持美观，并根据预测速度调整养护（如增加湿度可延缓衰老）。该技能可集成到智能花盆、园艺APP中，为用户提供精细化养护建议。

## 🤖 AI 角色 | AI Role
| 角色要点 | 说明 |
|---|---|
| 说明 1 | **假设你是一个专业的植物衰老预测 AI。你的任务是分析室内绿植的连续日间图像（至少过去 7 天，每天固定时间拍摄），检测叶片颜色（绿→黄→褐）、光泽度、叶柄角度等指标的变化趋势，基于时间序列模型预测未来数天内即将脱落（自然老化或胁迫导致）的叶片及时间窗口。不要提供具体的化学调控方法（如具体药剂剂量），仅输出预测结果与方向性养护建议。** |

## 🎬 技能演示 | Skill Demo

[▶️ 点击查看技能使用介绍](https://lifeemerge

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-leaf-aging-fall-prediction-analysis",
  "version": "1.0.16",
  "publishedAt": 1790993065726
}

references/api_doc.md

# API 接口文档

此处用于存放室内绿植叶片老化/脱落预测 API 的接口文档，待后续补充。

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 响应格式：JSON

## 主要接口

1. `/web/health-analysis/v2/start-health-analysis` - 启动叶片老化/脱落预测任务
2. `/web/health-analysis/v2/get-health-analysis-result` - 获取分析结果与脱落风险预测
3. `/web/health-analysis/page-health-analysis-result` - 分页查询历史预测记录
4. `/health/order/api/getReportDetailExport?id={id}` - 导出完整报告

## 场景代码

- `SMYX_LEAF_AGING_FALL_PREDICTION_ANALYSIS` - 室内绿植叶片老化/脱落预测

## 输入约束

- 推荐使用每天同一角度、同一时段的连续叶片图像（至少过去 7 天）
- 关键观测特征：叶色变化（绿→黄→褐）、叶面光泽度、叶柄基部离层角度
- 可选附带：植物名称/品种、放置环境（光照、湿度）、上次施肥/浇水时间

## 输出字段（参考）

- `aging_index` - 叶片老化指数（0-100%）
- `predicted_fall_window` - 预测脱落时间窗口（如 "未来 3-5 天"）
- `at_risk_leaves` - 即将脱落的叶片定位/编号
- `aging_cause_hint` - 老化原因提示（自然衰老 / 缺水胁迫 / 光照不足 / 营养缺乏等）
- `care_suggestion` - 养护方向建议（提高湿度、调整光照、修剪建议等，非具体化学方案）

skills/smyx_analysis/references/api_doc.md

# API接口文档

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 错误码说明

| 错误码 | 说明       |
|-----|----------|
| 400 | 请求参数错误   |
| 401 | API密钥无效  |
| 403 | 权限不足     |
| 413 | 文件过大     |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误  |

scripts/config.yaml

{}

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