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Weighted Tree-based Crop Classification Models for Imbalanced Datasets

Second place solution in the Zindi AgriFieldNet India Challenge to classify crop types in agricultural fields across Northern India using multispectral observations from Sentinel-2 satellite. Ensembled weighted tree-based models "LGBM, CATBOOST, XGBOOST" with stratified k-fold cross validation, taking advantage of spatial variability around each field within different distances.

model_ecaas_agrifieldnet_silver_v1

MLHub model id: model_ecaas_agrifieldnet_silver_v1. Browse on Radiant MLHub.

Training Data

Related MLHub Dataset

AgriFieldNet Competition Dataset

Citation

Alasawdah, M. "Weighted Tree-based Crop Classification Models for Imbalanced Datasets", Version 1.0, Radiant MLHub. [Date Accessed] Radiant MLHub https://doi.org/10.34911/rdnt.qiuwp5

License

CC-BY-4.0

Creator

Mohammad Alasawdah - Earth Observation and Climate Data Science https://www.linkedin.com/in/mohammad-alasawdah-b3b541a5/

Contact

[email protected]

Applicable Spatial Extent

The applicable spatial extent, for new inferencing.

{
    "type": "FeatureCollection",
    "features": [
        {
            "properties": {
                "id": "ref_agrifieldnet_competition_v1"
            },
            "type": "Feature",
            "geometry": {
                "type": "MultiPolygon",
                "bbox": [
                    76.2448,
                    18.9414,
                    88.046,
                    28.327
                ],
                "coordinates": [
                    [
                        [
                            [
                                88.046,
                                18.9414
                            ],
                            [
                                88.046,
                                28.327
                            ],
                            [
                                76.2448,
                                28.327
                            ],
                            [
                                76.2448,
                                18.9414
                            ],
                            [
                                88.046,
                                18.9414
                            ]
                        ]
                    ]
                ]
            }
        }
    ]
}
Loading

Applicable Temporal Extent

The recommended start/end date of imagery for new inferencing.

Start End
2022-01-01 present

Learning Approach

  • Supervised

Prediction Type

  • Classification

Training Operating System

  • Linux

Training Processor Type

  • CPU

Model Inferencing

Review the GitHub repository README to get started running this model for new inferencing.

Training

Prepare the data for tree models by computing the average values of the pixels within each field, then feature engineering by computing spatial variability, more vegetation, and flowering phenology indices.

Zonal statistics (mean , min, max, std) within different radiuses (0.50, 1.00, 1.50, 2.50, 3.50, 5.00) Km around each field

Model

Weighted average tree-based models: lightgbm. catboost, xgboost classifers.

Structure of Output Data

  • Predictions.csv: Final predictions text file, with 13 crops classes as following Wheat, Mustard, Lentil, No Crop, Sugarcane, Garlic, Potato, Green pea, Bersem, Coriander, Gram, Maize, Rice
  • veg_indices.csv: Extracted vegetation indices for each field.
  • Field_stats_indices.csv: Extracted statistics for each field.