Artificial intelligence enhances maize production through smart populations within planetary boundaries

# Description ## Raw Data Files ### 1. AIsp_origindata.xlsx- **Purpose**: Raw dataset for building the AIsp_TabPFN model- **Content**: Contains all original features and target variables used for AIsp model development---"""Soil: soil organic matter (SOM).Climate: average daily maximum temperature (Tmax), average daily minimum temperatures (Tmin), cumulative precipitation (Prep), cumulative solar radiation (Radn), and standardized precipitation–evapotranspiration index (SPEI).Management: Tillage and Irrigation.Thermal indices: growing degree days (GDD) and extreme degree days (EDD).Genotype: canopy architecture (Canopy). Target variable: optimal planting density (OPD).Note: Optimal yield (Yopt): This variable was not utilized in the modeling process, but subsequent steps require mapping from OPD to Yopt by canopy (smart vs. non-smart).---""" ### 2. AInm_origindata.xlsx- **Purpose**: Raw dataset for building the AInm_CatBoost model- **Content**: Contains all original features and target variables used for AInm model development---"""Soil: soil organic matter (SOM), soil total nitrogen (TotalN) and pH.Climate: average daily maximum temperature (Tmax), average daily minimum temperatures (Tmin), cumulative precipitation (Prep), cumulative solar radiation (Radn), and standardized precipitation–evapotranspiration index (SPEI).Management: Tillage and Irrigation.Thermal indices: growing degree days (GDD) and extreme degree days (EDD).Productivity: optimal yield (Yopt).Target variable: optimal nitrogen application (Nopt).---""" # Python Code Files ## 1. AIsp_compare.py- **Function**: Compares performance of different algorithms for AIsp model construction- **Process**: - Evaluates multiple machine learning algorithms - Identifies the optimal algorithm based on performance metrics - Outputs comparative analysis results ## 2. AIsp_TabPFN.py- **Function**: Implements the final AIsp model using the selected optimal algorithm- **Process**: - Performs 10-fold cross-validation with SHAP interpretation - Retrains the model using the entire dataset for final deployment - Generates model explanations and feature importance analysis ## 3. AInm_compare.py- **Function**: Compares performance of different algorithms for AInm model construction - **Process**: - Evaluates multiple machine learning algorithms - Identifies the optimal algorithm based on performance metrics - Outputs comparative analysis results ## 4. AInm_TabPFN.py- **Function**: Implements the final AInm model using the selected optimal algorithm- **Process**: - Performs 10-fold cross-validation with SHAP interpretation - Retrains the model using the entire dataset for final deployment - Generates model explanations and feature importance analysis