## 5 Conclusions  
Data assimilation has obvious advantages, great potential, and broad space for development in agricultural applications. However, the quality, resolution, and applicability of assimilation data sources are still the key bottlenecks affecting the development of data assimilation precision agriculture. In the future, the assimilation applications of emerging satellite constellation remote sensing, UAV, ground station data, high spatio-temporal resolution remote sensing, and multi-source remote sensing fusion technology should be vigorously promoted. It is worth noting that in light of the quantification of remote sensing integration errors and uncertainties, the introduction of the development agenda has an efficient and significant effect and is an effective means to promote the future development agenda of precision agriculture. The assimilation application of crop growth model is uneven. The SWAP model stands out in the application of agricultural data assimilation because of its wide applicability. The development trend of Aquacrop, WOFOST, and APSIM models is also quite positive, but the applications of Cropsyst and GLAM models are relatively few. The potential of multi-model fusion and assimilation mechanism is worthy of further study. LAI is the mainstream assimilation parameter selection, and the progress of multi-parameter data assimilation in precision agriculture is worth looking forward to. Model parameter optimization and uncertainty quantification mainly depend on the quantitative calibration method under the Bayesian framework. With the rise of ML technology, automatic parameter adjustment methods and integrated assimilation strategies based on ML, such as EnKF, variational assimilation, Bayesian filtering, and so on, show broad research prospects. In the application trend of precision agriculture assimilation model strategy, the data assimilation research based on EnKF is the most active, while the Bayesian system assimilation strategy shows a trend of rapid development. The development of multi-level assimilation or multi-model integrated assimilation is worth exploring. The data assimilation model is developing in the direction of refinement, diversification, and integration. Its mechanism framework has been continuously deepened and improved, resulting in a significant improvement in the overall performance. In the future, advances in GPU technology, GEE platform, cloud computing technology, and artificial intelligence will make large-scale parallel assimilation possible and release the great application potential of precision agriculture. However, the current research literature shows that it needs to be strengthened in the field of agricultural data assimilation. In order to meet the growing demand of data assimilation in precision agriculture, future research should focus on the optimization of assimilation data sources, the refinement of crop growth models, and the upgrading of assimilation model strategies to improve adaptability, accuracy, and efficiency. At present, there is an imbalance in spatial and regional distribution in the research of agricultural data assimilation, so it is very important to strengthen the sharing and exchange of international data assimilation results. It is necessary to promote cooperative research between countries and regions, share advanced experience and technological innovations, and promote the innovative development of global agricultural data assimilation. Conceptualization, Z.Q. and Y.W. ; methodology, Y.W. ; software, Y.W. ; validation, J.W. and Y.W. ; formal analysis, Z.Q. ; investigation, Z.Q. ; resources, Y.W. ; data curation, J.W. ; writing--original draft preparation, J.W. ; writing--review and editing, J.W. ; visualization, Y.W. ; supervision, Y.W. ; project administration, J.W. ; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.