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#

bash

python <SKILL_DIR>/scripts/check_env.py

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

bash

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#

CodeChineseEnglishUnitHD抽穗期Heading DatedaysPH株高Plant HeightcmPL穗长Panicle LengthcmTN分蘖数Tiller NumbercountGP每穗粒数Grains Per PaniclecountSSR结实率Seed Setting Rate%TGW千粒重Thousand Grain WeightgGL粒长Grain LengthmmGW粒宽Grain WidthmmY产量Yieldkg/ha

Supported Locations (7 built-in stations)#

CodeCityLatLonkm昆明25.02102.68gzl六盘水26.59104.83nn南宁22.82108.37wh武汉30.58114.27hf合肥31.82117.25hz杭州30.25120.17th通化41.73125.94

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#

TypeChineseDefault effecthigh_temp高温胁迫+3°C max / +2°C minlow_temp低温胁迫-3°C max / -2°C mindrought干旱胁迫90% precipitation reductionflood涝害胁迫3x precipitation increaselow_light寡照胁迫60% PAR reduction

Prediction Commands#

Full prediction (recommended)#

bash

python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1

Genotype-only / environment-only#

bash

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#

bash

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

With stress#

bash

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#

bash

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

Custom genotype file#

bash

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#

bash

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#

bash

python <SKILL_DIR>/scripts/predict.py --lat 30.5 --lon 114.3 --sample sample1 --output table

All CLI arguments#

ArgDefaultDescription--latrequiredLatitude--lonrequiredLongitude--sampleNoneBuilt-in sample ID(s), comma-separated (sample1..sample3925)--genotype_fileNoneCustom 1024-dim VAE CSV path--modefullgene, env, or full``--traitallComma-separated trait codes or all``--stressNoneStress type name--stress_deltaNoneOverride temperature delta--deviceautoauto, cpu, or cuda:0``--year2024Year for environmental data--outputjsonjson or table``--base_dirautoOverride 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#

text

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)