Install
openclaw skills install gainGAIN
Predict rice agronomic traits (yield, plant height, heading date, grain size, etc.) from genotype and environmental data using pre-trained MMoE deep learning models. Use when the user asks about rice phenotype prediction, crop trait estimation, genotype-environment interaction, or environmental stress effects on rice. Supports Chinese and English. Trigger terms: 水稻, 表型, 预测, 株高, 产量, 粒长, 抽穗期, 千粒重, 结实率, rice, phenotype, yield, trait, stress.
Rice Phenotype Prediction
Self-contained skill for predicting 10 rice agronomic traits via pre-trained MMoE models. All models, data, and scripts are inside this directory — give users this one folder.
Setup
First-time check
python <SKILL_DIR>/scripts/check_env.py
This verifies Python dependencies and data integrity. If packages are missing:
pip install -r <SKILL_DIR>/requirements.txt
Required: torch>=2.0 numpy pandas scikit-learn scipy requests
GPU is optional — CPU works (just slower). If GPU is present, cuda:0 is used automatically.
<SKILL_DIR> convention
Throughout this file, <SKILL_DIR> means the absolute path to this skill's root directory
(the folder containing this SKILL.md). When running commands, substitute with the actual path.
--base_dir is optional; if omitted, scripts auto-detect it from their own location.
Supported Traits
| Code | Chinese | English | Unit |
|---|---|---|---|
| HD | 抽穗期 | Heading Date | days |
| PH | 株高 | Plant Height | cm |
| PL | 穗长 | Panicle Length | cm |
| TN | 分蘖数 | Tiller Number | count |
| GP | 每穗粒数 | Grains Per Panicle | count |
| SSR | 结实率 | Seed Setting Rate | % |
| TGW | 千粒重 | Thousand Grain Weight | g |
| GL | 粒长 | Grain Length | mm |
| GW | 粒宽 | Grain Width | mm |
| Y | 产量 | Yield | kg/ha |
Supported Locations (7 built-in stations)
| Code | City | Lat | Lon |
|---|---|---|---|
| km | 昆明 | 25.02 | 102.68 |
| gzl | 六盘水 | 26.59 | 104.83 |
| nn | 南宁 | 22.82 | 108.37 |
| wh | 武汉 | 30.58 | 114.27 |
| hf | 合肥 | 31.82 | 117.25 |
| hz | 杭州 | 30.25 | 120.17 |
| th | 通化 | 41.73 | 125.94 |
Any input lat/lon is auto-matched to the nearest station via Haversine distance. For locations with internet, daily weather data can also be fetched from NASA POWER API for the exact coordinates.
Stress Types
| Type | Chinese | Default effect |
|---|---|---|
| high_temp | 高温胁迫 | +3°C max / +2°C min |
| low_temp | 低温胁迫 | -3°C max / -2°C min |
| drought | 干旱胁迫 | 90% precipitation reduction |
| flood | 涝害胁迫 | 3x precipitation increase |
| low_light | 寡照胁迫 | 60% PAR reduction |
Prediction Commands
Full prediction (recommended)
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1
Genotype-only / environment-only
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --mode gene
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --mode env
Specific traits
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --trait PH,Y
With stress
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --stress high_temp
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --stress high_temp --stress_delta 5.0
Multiple samples
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample "sample1,sample2,sample3"
Custom genotype file
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --genotype_file /path/to/user_vae.csv
Format: CSV with 1024 columns (VAE-encoded features), first column = sample index.
Force CPU / specific device
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --device cpu
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --device cuda:0
Human-readable table
python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --output table
All CLI arguments
| Arg | Default | Description |
|---|---|---|
--lat | required | Latitude |
--lon | required | Longitude |
--sample | None | Built-in sample ID(s), comma-separated (sample1..sample3925) |
--genotype_file | None | Custom 1024-dim VAE CSV path |
--mode | full | gene, env, or full |
--trait | all | Comma-separated trait codes or all |
--stress | None | Stress type name |
--stress_delta | None | Override temperature delta |
--device | auto | auto, cpu, or cuda:0 |
--year | 2024 | Year for environmental data |
--output | json | json or table |
--base_dir | auto | Override skill directory path |
Handling User Requests
1. Extract location
- "经纬度30.5, 114.3" →
--lat 30.5 --lon 114.3 - "武汉" →
--lat 30.58 --lon 114.27 - "北纬25度，东经103度" →
--lat 25 --lon 103
2. Map trait names
- 株高/plant height → PH
- 产量/yield → Y
- 粒长/grain length → GL
- 抽穗期/heading date → HD
- 千粒重/1000-grain weight → TGW
- 穗长/panicle length → PL
- 结实率/seed setting rate → SSR
- 每穗粒数/grains per panicle → GP
- 粒宽/grain width → GW
- 分蘖数/tiller number → TN
3. Map stress requests
- 高温/heat → high_temp
- 低温/cold/chilling → low_temp
- 干旱/drought → drought
- 洪涝/flooding → flood
- 阴天/寡照/low light → low_light
- "高温+5度" →
--stress high_temp --stress_delta 5.0
4. Genotype data
- Built-in samples:
--sample sample1(3925 available: sample1..sample3925) - User file:
--genotype_file /path/to/file.csv
5. Interpreting output
JSON contains: location, genotype_prediction, environment_prediction, stress_prediction, trait_info.
Report environment_prediction as primary (has environmental context).
Compare genotype_prediction as baseline.
For stress, compare normal vs stressed values.
Rounding: HD/TN/GP → integer, PH/PL/TGW/SSR → 1 decimal, GL/GW → 2 decimals, Y → integer.
Directory Structure
rice_prediction/ ← give users this folder
├── SKILL.md ← this file
├── requirements.txt ← pip dependencies
├── data/
│ ├── grid_points.json ← 7 station coordinates
│ ├── vae_features.csv ← 3925 built-in genotype samples (1024-dim VAE)
│ ├── season_history.csv ← historical season data for normalization
│ ├── env_cache/ ← cached daily weather (auto-populated)
│ ├── models_env/ ← 10 trait-specific env+gene models (~4.6MB each)
│ └── models_gene/ ← 7 location-specific genotype models (~8MB each)
└── scripts/
├── predict.py ← main entry point
├── check_env.py ← dependency checker
├── model_def.py ← MMoE model architectures
├── grid_manager.py ← nearest grid point finder
├── env_data_fetcher.py ← NASA POWER API fetcher + cache
├── env_processor.py ← environmental feature engineering
└── stress_simulator.py ← stress scenario simulation
Architecture (for reference)
- Model: Multi-gate Mixture-of-Experts (MMoE) with ResidualMLP experts
- Genotype features: 1024-dim VAE latent encoding of genomic data
- Environment features: 53 season-aggregated variables from daily weather
- Environmental data: NASA POWER API (auto-fetched and cached locally)