Install
openclaw skills install smyx-leaf-aging-fall-prediction-analysisUsing 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。
openclaw skills install smyx-leaf-aging-fall-prediction-analysisUsing 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。
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。
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --list --open-id 参数调用 API
查询云端的历史报告数据requests>=2.28.0
在执行叶片老化/脱落预测前,必须按以下优先级顺序获取 open-id:
第 1 步:【最高优先级】检查技能所在目录的配置文件(优先)
路径:skills/smyx_common/scripts/config.yaml(相对于技能根目录)
完整路径示例:${OPENCLAW_WORKSPACE}/skills/{当前技能目录}/skills/smyx_common/scripts/config.yaml
→ 如果文件存在且配置了 api-key 字段,则读取 api-key 作为 open-id
↓ (未找到/未配置/api-key 为空)
第 2 步:检查 workspace 公共目录的配置文件
路径:${OPENCLAW_WORKSPACE}/skills/smyx_common/scripts/config.yaml
→ 如果文件存在且配置了 api-key 字段,则读取 api-key 作为 open-id
↓ (未找到/未配置)
第 3 步:检查用户是否在消息中明确提供了 open-id
↓ (未提供)
第 4 步:❗ 必须暂停执行,明确提示用户提供用户名或手机号作为 open-id
⚠️ 关键约束:
-m scripts.smyx_leaf_aging_fall_prediction_analysis 处理输入(必须在技能根目录下运行脚本)--input: 本地叶片图像序列/视频文件路径--url: 网络叶片图像序列/视频 URL 地址(API 服务自动下载)--pet-type: 类别标识,植物场景默认 other--open-id: 当前用户的 open-id(必填,按上述流程获取)--list: 显示叶片老化/脱落预测历史分析报告列表清单(可以输入起始日期参数过滤数据范围)--api-key: API 访问密钥(可选)--api-url: API 服务地址(可选,使用默认值)--detail: 输出详细程度(basic/standard/json,默认 json)--output: 结果输出文件路径(可选)叶片老化脱落预测报告-{记录id}形式拼接, "点击查看"
列使用
[🔗 查看报告](reportImageUrl)
格式的超链接,用户点击即可直接跳转到对应的完整报告页面。| 报告名称 | 植物种类 | 分析时间 | 点击查看 |
|---|---|---|---|
| 叶片老化脱落预测报告-20260312172200001 | 富贵竹 | 2026-03-12 17:22:00 | 🔗 查看报告 |
# 分析本地叶片图像序列(以下只是示例,禁止直接使用openclaw-control-ui 作为 open-id)
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --input /path/to/leaf_sequence.mp4 --open-id your-open-id
# 分析网络叶片视频(以下只是示例,禁止直接使用openclaw-control-ui 作为 open-id)
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --url https://example.com/leaf_sequence.mp4 --open-id your-open-id
# 显示历史脱落预测报告/落叶预测报告清单(自动触发关键词:查看叶片老化历史报告、落叶预测报告清单等)
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --list --open-id your-open-id
# 输出精简报告
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --input video.mp4 --open-id your-open-id --detail basic
# 保存结果到文件
python -m scripts.smyx_leaf_aging_fall_prediction_analysis --input video.mp4 --open-id your-open-id --output result.json