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: CODEBLOCK1

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
hightemp	高温胁迫	+3°C max / +2°C min
lowtemp

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)

CODEBLOCK2

Genotype-only / environment-only

CODEBLOCK3

Specific traits

CODEBLOCK4

With stress

CODEBLOCK5

Multiple samples

CODEBLOCK6

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

CODEBLOCK8

Human-readable table

CODEBLOCK9

All CLI arguments

Arg	Default	Description
INLINECODE5	required	Latitude
INLINECODE6

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" → INLINECODE26
• "武汉" → INLINECODE27
• "北纬25度，东经103度" → INLINECODE28

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 → hightemp
• 低温/cold/chilling → lowtemp
• 干旱/drought → drought
• 洪涝/flooding → flood
• 阴天/寡照/low light → low_light
• "高温+5度" → INLINECODE29

4. Genotype data

• - Built-in samples: --sample sample1 (3925 available: sample1..sample3925)
• User file: INLINECODE31

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

CODEBLOCK10

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)