UniCrop自动整合多源数据，实现可扩展的作物产量预测。
UniCrop: A Universal, Multi-Source Data Engineering Pipeline for Scalable Crop Yield Prediction
- 构建通用数据管道，自动处理卫星、气象等多源环境数据
- 仅用15个特征即达RMSE=463.2 kg/ha，R²=0.6604
- 支持任意作物、区域和时间，配置即可复用
精准作物产量预测依赖于卫星、气象、土壤和地形等多种数据流。然而，现有方法仍局限于特定作物或区域，需大量数据工程工作，限制了可扩展性、可复现性和实际部署。本研究提出UniCrop，一种通用可复用的数据处理管道，可自动获取、清洗、统一并工程化多源环境数据，用于作物产量预测。针对任意位置、作物类型和时间窗口，UniCrop自动整合超过200个环境变量（包括Sentinel-1/2、MODIS、ERA5-Land、NASA POWER、SoilGrids和SRTM），通过最小冗余最大相关（mRMR）结构化特征筛选流程，生成紧凑且分析就绪的特征集。为验证效果，将UniCrop应用于含557个田间观测点的水稻产量数据集，仅使用15个特征，四种基线模型（LightGBM、随机森林、支持向量回归、弹性网络）训练后，LightGBM表现最佳（RMSE=465.1 kg/ha，R²=0.6576），约束集成进一步提升至RMSE=463.2 kg/ha，R²=0.6604。UniCrop提供可扩展、透明的数据工程框架，解决了作物产量建模中的主要瓶颈——多源数据的一致性与标准化准备问题。通过解耦数据定义与实现，支持任意作物、区域和时间范围，只需简单配置更新，为规模化农业分析提供实用基础。代码与文档见https://github.com/CoDIS-Lab/UniCrop。
原文摘要 · Abstract (English)
Accurate crop yield prediction relies on diverse data streams, including satellite, meteorological, soil, and topographic information. However, despite rapid advances in machine learning, existing approaches remain crop- or region-specific and require data engineering efforts. This limits scalability, reproducibility, and operational deployment. This study introduces UniCrop, a universal and reusable data pipeline designed to automate the acquisition, cleaning, harmonisation, and engineering of multi-source environmental data for crop yield prediction. For any given location, crop type, and temporal window, UniCrop automatically retrieves, harmonises, and engineers over 200 environmental variables (Sentinel-1/2, MODIS, ERA5-Land, NASA POWER, SoilGrids, and SRTM), reducing them to a compact, analysis-ready feature set utilising a structured feature reduction workflow with minimum redundancy maximum relevance (mRMR). To validate, UniCrop was applied to a rice yield dataset comprising 557 field observations. Using only the selected 15 features, four baseline machine learning models (LightGBM, Random Forest, Support Vector Regression, and Elastic Net) were trained. LightGBM achieved the best single-model performance (RMSE = 465.1 kg/ha, $R^2 = 0.6576$), while a constrained ensemble of all baselines further improved accuracy (RMSE = 463.2 kg/ha, $R^2 = 0.6604$). UniCrop contributes a scalable and transparent data-engineering framework that addresses the primary bottleneck in operational crop yield modelling: the preparation of consistent and harmonised multi-source data. By decoupling data specification from implementation and supporting any crop, region, and time frame through simple configuration updates, UniCrop provides a practical foundation for scalable agricultural analytics. The code and implementation documentation are shared in https://github.com/CoDIS-Lab/UniCrop.
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