融合 CWSI 水分胁迫、红边叶绿素与 SAR 含水量，检测作物胁迫等级。Detects crop stress by fusing CWSI water stress, red-edge chlorophyll and SAR water content.
编程
geoskill-pest-disease-detection
试用基于红边异常、热红外温度、纹理变化与多时相早期胁迫检测，识别疑似病虫害区域。Detects suspected pest/disease areas from red-edge anomaly, thermal, texture and multi-temporal early stress.
它能做什么
基于红边异常、热红外温度、纹理变化与多时相早期胁迫检测，识别疑似病虫害区域。Detects suspected pest/disease areas from red-edge anomaly, thermal, texture and multi-temporal early stress.
技能文档
病虫害遥感检测 | Pest and Disease Detection
(Fill in 2-3 paragraphs of Chinese introduction here: functionality, application scenarios, core algorithm.)
Dependencies / 依赖
pip install 'numpy' 'rasterio' 'scipy'
Usage / 使用方法
Basic usage
python geoskill-pest-disease-detection.py --bbox 116.0 39.0 117.0 40.0 [other options]
Example 1 (synthetic data, offline)
python geoskill-pest-disease-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Examples 2-5
(Add at least 4 real-world usage examples.)
Output / 输出
| File | Format | Description |
|---|---|---|
result.tif | GeoTIFF | Main output |
output-manifest.json | JSON | Run manifest |
Data Source / 数据源 / Source
(Describe the data source: free satellite data / local input / synthetic.)
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network at all. - All processing is performed locally; user data is never uploaded.
License / License
MIT
name: geoskill-pest-disease-detection description: '基于红边异常、热红外温度、纹理变化与多时相早期胁迫检测，识别疑似病虫害区域。Detects suspected pest/disease areas from red-edge anomaly, thermal, texture and multi-temporal early stress.'
病虫害遥感检测 | Pest and Disease Detection
（在此填写 2-3 段中文介绍：功能、应用场景、核心算法。）
依赖
pip install 'numpy' 'rasterio' 'scipy'
使用方法
基本用法
python geoskill-pest-disease-detection.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
示例 1（合成数据，离线）
python geoskill-pest-disease-detection.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2-5
（补充至少 4 个真实用法示例。）
输出
| 文件 | 格式 | 说明 |
|---|---|---|
result.tif | GeoTIFF | 主产物 |
output-manifest.json | JSON | 运行清单 |
数据源 / Source
（说明数据来源：免费卫星数据 / 本地输入 / 合成。）
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传用户数据。
License
MIT
相关技能
AI-powered pest & disease detection for roses (Rosa spp.). From garden cameras or mobile phone images of leaves, young shoots and flower buds, detects common rose enemies including black spot (black round/irregular spots with yellow halo), powdery mildew (white powdery layer on leaves/shoots), spider mites (tiny red/white dots on leaf back with webbing in severe cases) and aphids (green/black clustered tiny insects on shoots and buds). Outputs pest/disease type, severity grade and general control suggestions. Helps gardeners detect issues early and act in time. Scenarios: home gardens, rose specialty gardens, courtyard landscaping, cut-flower production bases. | 通过庭院摄像头或手机拍摄月季/玫瑰的叶片、嫩芽、花苞图像，利用AI视觉分析技术检测黑斑病（叶面黑色圆形或不规则斑点，周围黄晕）、白粉病（叶片、嫩芽表面白色粉状霉层）、红蜘蛛（叶片背面细小红色或白色点状螨虫，严重时结网）、蚜虫（嫩芽、花苞上绿色或黑色聚集的小虫）等常见病虫害，输出病虫害类型及严重程度，并提供防治建议。该技能有助于月季种植者早期发现问题，及时采取措施。应用场景：家庭花园、月季专类园、庭院绿化、切花生产基地。
11 次安装
融合 NDVI/NDRE/LST 多指数健康评分、历史偏差异常检测与空间聚类，输出作物健康等级栅格。Fuses NDVI/NDRE/LST into a health score with historical anomaly detection and spatial clustering.
Triggers when a user provides images or videos of crop leaves, buds or fruits (local file or URL) for pest identification; calls server-side APIs to detect common agricultural pests such as aphids, red spider mites, cotton bollworms and corn borers, outputting pest types with confidence scores. | 当用户提供作物叶片、嫩芽或果实的图像/视频（本地文件或网络URL）时，触发本技能进行虫害识别；调用服务端API检测蚜虫、红蜘蛛、棉铃虫、玉米螟等常见农业害虫，输出虫害类型与置信度。应用场景：番茄/玉米/花生的虫害早期发现与精准施药，减少农药滥用。
多时相指数阈值分类检测入侵新增像元，计算面积相对扩散速率，用环境适宜性×距离衰减预测入侵风险。Monitors invasive species spread from multi-temporal classification and risk prediction. 输出新增入侵与风险 GeoTIFF。
1 次安装
高分辨率作物识别叠加申报地块做差异检测，核查补贴合规性。Verifies subsidy compliance by overlaying high-resolution crop classification on declared parcels for difference detection.