TY  - JOUR
AU  - Wei, Yongqiang
AU  - Li, Yiyang
AU  - Xu, Zhaoxing
AU  - Wang, Bin
AU  - Zhang, Gesen
PY  - 2026
DA  - 2026/05/06
TI  - Research on the Application of Agricultural Big Data in Plant Growth Prediction
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 2
IS  - 2
SP  - 54
EP  - 67
DO  - 10.62762/DIA.2025.779448
UR  - https://www.icck.org/article/abs/DIA.2025.779448
KW  - agricultural big data
KW  - plant growth prediction
KW  - precision agriculture
KW  - hybrid AI model
KW  - data assimilation
KW  - sustainable intensification
KW  - digital twin
AB  - The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Wei2026Research,
  author = {Yongqiang Wei and Yiyang Li and Zhaoxing Xu and Bin Wang and Gesen Zhang},
  title = {Research on the Application of Agricultural Big Data in Plant Growth Prediction},
  journal = {Digital Intelligence in Agriculture},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {54-67},
  doi = {10.62762/DIA.2025.779448},
  url = {https://www.icck.org/article/abs/DIA.2025.779448},
  abstract = {The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7\% for yield prediction and 38.1\% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5\% and irrigation water by 18.3\% while increasing yield by 5.1\%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.},
  keywords = {agricultural big data, plant growth prediction, precision agriculture, hybrid AI model, data assimilation, sustainable intensification, digital twin},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}