**6. Conclusions**

This paper presents a new in-field phenotyping paradigm. An interactive cognition method is proposed to overcome the problem of occlusion and overlap in traditional passive automatic phenotyping methods. A bio-inspired solution is introduced so that the phenotyping robot can mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of full growth cycles is realized. A tiller number recognition method (AtResNet) is proposed based on interactive cognition. In-field images are collected for the experiments. The experiment results show that the proposed method can achieve approximately 95% tiller number recognition accuracy and outperforms other deep learning-based methods. This paper provides a new solution to the occlusion and observation pose problems in field phenotyping. Although drone detection can estimate the panicle number in a more efficient way, the proposed method overcomes the difficulty of under-canopy tiller counting, which assists in effective and ineffective tillering counting. Compared with traditional manual breeding processes, the proposed in-field phenotyping paradigm offers a more efficient solution to repeating phenotyping across the full growth period. In future work, we will develop multiple phenotyping robots and explore the control scheme of switching between them to further improve in-field phenotyping efficiency. Moreover, the panicle counting method based on drone detection over the canopy will be studied to estimate effective tillering.

**Author Contributions:** Conceptualization, Y.H. and L.G.; methodology, P.X. and L.G.; software, Y.H. and P.X.; validation, Y.L. and B.C.; data curation, Y.H., P.X. and L.G.; writing—original draft preparation, Y.H., P.X. and L.G.; writing—review and editing, L.G. and C.L.; visualization, P.X.; project

administration, C.L.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program under Grant No. 2019YFE0125200.

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

**Data Availability Statement:** The data presented in this study are available from the corresponding authors upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.
