BioSciAdmin BioSci

Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No.   
Received: 01 Jan., 1970    Accepted: 01 Jan., 1970    Published: 21 Sep., 2026

The integration of field observation data and computational methods provides a powerful approach for optimizing crop management under increasingly complex agricultural environments. This review summarizes recent advances in the acquisition, processing, and application of multi-source field data, including soil properties, climatic conditions, remote sensing information, and crop growth indicators. Statistical models, machine learning algorithms, deep learning approaches, and crop process-based models have been widely applied to reveal crop–environment interactions, predict yield performance, and support precision management decisions. Data assimilation and hybrid modeling strategies further enhance the accuracy and reliability of agricultural prediction systems by combining empirical observations with mechanistic knowledge. Case studies demonstrate the effectiveness of data-driven approaches in yield forecasting, precision water and fertilizer management, and crop stress identification. Despite significant progress, challenges remain in data quality control, model transferability, and the integration of artificial intelligence with agricultural knowledge. Future research should focus on developing intelligent, explainable, and scalable agricultural management systems to improve resource-use efficiency, crop productivity, and sustainable agricultural development.