GAIN

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name: rice-phenotype-prediction description: >- 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.

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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 /scripts/check_env.py

This verifies Python dependencies and data integrity. If packages are missing:

pip install -r /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.

convention

Throughout this file, 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 /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1

Genotype-only / environment-only

python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --mode gene
python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --mode env

Specific traits

python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --trait PH,Y

With stress

python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --stress high_temp
python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --stress high_temp --stress_delta 5.0

Multiple samples

python /scripts/predict.py --lat 30.5 --lon 114.3 --sample "sample1,sample2,sample3"

Custom genotype file

python /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 /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --device cpu
python /scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --device cuda:0

Human-readable table

python /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)

First-time check

python /scripts/check_env.py

This verifies Python dependencies and data integrity. If packages are missing:

pip install -r /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.

convention

Throughout this file, 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.