# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 5 Conclusions  
The utilization of the improved YOLOv7 model for detecting the coloring and ripening periods of dwarf rootstock apples contributes to effective fruit tree harvest and precise greenhouse management. It promotes the green development of fruit production and strengthens the innovation of smart agricultural technology, thus making a positive contribution to the sustainable development of the apple industry. The proposed model uses an efficient, parameterizable backbone network called RepBlock for lightweight networks, introduces the SE attention mechanism to optimize features, and uses the SloU loss function to improve the ability to detect obscured apples. The proposed model shows significant improvements in precision, recall, and average accuracy rate, while being lightweight. Compared to the YOLOv7 model, it has achieved remarkable increases of 4.88%, 1.18%, and 5.61% in precision, recall, and average accuracy rate, respectively. In conclusion, the proposed model exhibits significant potential and advantages in detecting the coloring and maturity stages of dwarf rootstock apples. In future research, further optimization of the detection model can enhance its precision and real-time performance. Further research on deploying the improved model into practical applications, such as quantitative fertilization during the coloring stage and real-time harvesting during the ripening periods, can save resources, protect the environment, and improve farming efficiency, thereby promoting the sustainable development of agriculture. Future research and applications will drive advancements in smart agriculture, offering precise management of resources like water, soil, and fertilizers. This reduces biochemical use, lowers labor intensity, and promotes sustainable agricultural development.  
Conceptualization, H.M. and X.Z.; methodology, H.M., X.Z. and Y.L. ([PERSON]); software, H.M. and Y.L. ([PERSON]); validation, H.M., Y.L. ([PERSON]) and Y.L. ([PERSON]); resources, H.M. and X.Z.; data curation, H.M., Y.L. ([PERSON]) and X.Z.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and X.Z.; visualization, H.M., Y.L. ([PERSON]), Z.L., Q.Z., R.Z. and R.H.; supervision, X.Z. and Y.L. ([PERSON]); funding acquisition, H.M. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Key Research and Development Project in Shanxi Province (No. 202202140601021) and the guiding fund of the award-winning project of \"Internet +\" Competition of Shanxi Agricultural University (23142N02180001).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Figure 14: PR curves of different detection models.  
**Acknowledgments:** We are deeply grateful to [PERSON] and [PERSON] (Cotton Research Institute, Shanxi Agricultural University) for their valuable support, providing us with an experimental platform and providing us with valuable insights. We would like to thank [PERSON] (Shanxi Fruit tree Research Institute) for his help in providing experimental materials and data sampling. Their participation is essential to ensure the quality and accuracy of our research results.  
**References**