**5. Conclusions**

A feed rate measurement method based on multi-component power monitoring, effective data automatic screening, and multivariate data fusion regression was studied in this paper, and field experiments were carried out to validate the model. The conclusions are as follows:

(1) The Mann-Kendall algorithm is applied to realize the automatic boundary extraction in the condition data filtering, which is highly applicable in the shredding rollers and throwing blower with a stable load. The data screening and pre-processing for the whole condition data are then achieved effectively in combination with the material-flow time lag correction model.

(2) The Pearson correlation analysis shows that the correlation coefficients between the feeding rate and the power data of the outside header, throwing blower, and shedding roller are all higher than 0.85, indicating a strong relation, which can be used to build a multivariate fusion feeding rate model.

(3) The multivariate least squares feeding rate model is obtained with excellent significance verification results. The maximum absolute error is 0.58 kg/s, while the maximum relative error is ±5.84%.

(4) As for the feeding rate measurement means of the maize silage harvester, if a single parameter methodology is considered, it is proposed to take shredding power as the input of the model, which provides good measurement accuracy. If a multiple conditions parameter methodology is used, the model based on the fusion of cutting power, shredding power, and blowing power can achieve a better detection result. Furthermore, there is still much room for the improvement of model accuracy and robustness by integrating crop characteristics.

**Author Contributions:** Conceptualization, B.Z. and Y.L.; methodology, F.W. and H.J.; software, F.W. and Y.L.; validation, F.W. and Y.J.; formal analysis, Y.J.; investigation, J.W.; resources, W.M.; writing original draft preparation, F.W.; writing—review and editing, F.W., B.Z., Y.L. and W.M.; visualization, H.J. and J.W.; supervision, Y.J.; project administration, W.M.; funding acquisition, B.Z. and W.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is supported by the National Key Research and Development China Project (2020YFB1709603).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** The original data supporting the study in this article are included in the article; further inquiries can be directed to the corresponding author.

**Acknowledgments:** The authors thank Shandong Wuzheng Group Co., Ltd. and the Experimental Base of Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd. for providing the experimental equipment.

**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
