## 5 Conclusions  
Sustainability in agricultural supply is crucial for ensuring food safety and improving people's lives, and detecting various logistics targets through remote sensing imagery is meaningful for improving the efficiency of logistics supervision. However, traditional detection methods have problems with horizontal labels ignoring the multi-scale variation and dense distribution of transportation equipment in the detection process of remote sensing imagery. To address the identified issues, this paper proposes EYMR-Net, an enhanced YOLO architecture detector tailored for multi-scale rotating remote sensing objects. The EYMR-Net incorporates several key advancements. Firstly, it features a novel backbone based on the Swin Transformer, which captures richer gradient fusion information of multi-scale targets, with a particular emphasis on enhancing the detection of small targets. Additionally, the model integrates the Convolutional Block Attention Module (CBAM) within the neck component, thereby improving feature analysis capabilities and reducing missed detections and omissions of small targets. Moreover, EYMR-Net introduces an innovative rotating frame regression mechanism by utilizing circular smoothing labels, effectively addressing the limitations of traditional horizontal regression models that often overlook target directionality. Extensive comparative and ablation experiments conducted on the DOTA dataset demonstrate the superior performance of EYMR-Net over competing models, achieving Precision, Recall, and mAP0.5 scores of 79.76%, 72.18%, and 74.30%, respectively. These results highlight EYMR-Net's innovative approach to multi-scale target detection, making it particularly well suited for practical monitoring applications of remote sensing technology.  
In the future, we will further explore the potential of the EYMR-Net model in other research fields, such as agricultural or pharmaceutical logistics. Meanwhile, we can further optimize related technologies and modeling strategies to enhance their performance in different remote sensing scenarios.  
Conceptualization, S.B.; methodology, S.B. and M.Z.; software, R.X.; validation, R.X.; formal analysis, S.B. and M.Z.; investigation and data curation, M.Z.; writing--original draft preparation, S.B.; writing--review and editing, J.K.; visualization, resource, S.B. and D.H.; supervision, project administration, J.K. and D.H. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Confusion matrix of EYMR-Net detector.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Nos. 62473008, 62433002, 62476014 and 62173007), Project of ALL China Federation of Supply and Marketing Cooperatives (No.202407), the Project of Beijing Municipal University Teacher Team Construction Support Plan (No. BPHR20220104), and the Beijing Scholars Program (No. 099).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset presented in this study is available at the following website link: [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)) accessed on 26 August 2024.  
**Conflicts of Interest:** The authors declare no conflicts of interest.