International Journal of Life Science and Agriculture Research

In the context of the rapid development of agricultural intelligence, the use of target recognition technology to detect grain phenomena can effectively ensure food safety. Synthetic aperture radar (SAR) technology has been widely used in the field of target recognition due to its high resolution and good anti - jamming ability. In this study, a SAR target recognition method based on electromagnetic scattering features is proposed. This method fuses the deep-learning algorithm YOLOv5 with electromagnetic scattering features to achieve target recognition and classification, and applies it to soybean classification and detection, aiming to provide an efficient and reliable solution for grain detection. Through theoretical analysis and experimental verification, this study demonstrates that the proposed method achieves a high maximum accuracy of 95.7% in identifying complex targets such as soybeans, indicating its excellent performance.

Electromagnetic Scattering Feature, SAR Target Recognition, Yolov5, Soybean Detection.

1. Jiayue, He, Nan, S. U., Cong’an, X. U., Lu, Y. I. N., Yanping, L. I. A. O., & Yiming, Y. A. N. (2024). From optical to SAR: A SAR ship detection algorithm based on multi-level cross-modality alignment. National Remote Sensing Bulletin, 28(7), 1789-1801.
2. Zhang Z.2023.Research on SAR Image Features and Scattering Characteristics under Deep     Learning Framework.Hangzhou:Zhejiang University.
3. Ding, B., Wen, G., Ma, C., & Yang, X. (2018). An efficient and robust framework for SAR target recognition by hierarchically fusing global and local features. IEEE Transactions on Image Processing, 27(12), 5983-5995.
4. Ding, B., Wen, G., Zhong, J., Ma, C., & Yang, X. (2017). A robust similarity measure for attributed scattering center sets with application to SAR ATR. Neurocomputing, 219, 130-143.
5. Wu W Q,Chen H J,Li Y F,Chen Z W and Sun J.2024.ESFFM:an electromagnetic scattering feature fusion model for SAR automatic target recognition.IEEE Sensors Journal,24(19):30424-30434 [DOI:10.1109/JSEN.2024.3440016]
6. Xing M D,Han Qing,Zhang Jinsong. (2025). Review of SAR Target Recognition Methods Based on Fusion of Electromagnetic Scattering Features. Journal of Remote Sensing, 29(6).
7. Zhang, T., & Zhang, X. (2019). High-speed ship detection in SAR images based on a grid convolutional neural network. Remote Sensing, 11(10), 1206.
8. Zhao, C., Zhang, S., Luo, R., Feng, S., & Kuang, G. (2023). Scattering features spatial-structural association network for aircraft recognition in SAR images. IEEE Geoscience and Remote Sensing Letters, 20, 1-5.
9. Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., ... & Wei, S. (2021). SAR ship detection dataset (SSDD): Official release and comprehensive data analysis. Remote Sensing, 13(18), 3690.
10. Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., ... & Kumar, D. (2020). LS-SSDD-v1. 0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR images. Remote Sensing, 12(18), 2997.
11. Sun X,Wang Z R,Sun Y R,Diao W H,Zhang Y and Fu K.2019.AIR SARShip-1.0:high-resolution SAR ship detection dataset.Journal of Radars,8(6):852-862[DOI:10.12000/JR 19097
12. Samadi, F., Akbarizadeh, G., & Kaabi, H. (2019). Change detection in SAR images using deep belief network: A new training approach based on morphological images. IET Image Processing, 13(12), 2255-2264.
13. Sunkara, R., & Luo, T. (2022, September). No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. In Joint European conference on machine learning and knowledge discovery in databases (pp. 443-459). Cham: Springer Nature Switzerland.
14. Huang, Z., Datcu, M., Pan, Z., & Lei, B. (2020). Deep SAR-Net: Learning objects from signals. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 179-193.
15. Shin, D. W., Yang, C. S., & Chowdhury, S. J. K. (2024). Enhancement of small ship detection using polarimetric combination from Sentinel− 1 imagery. Remote Sensing, 16(7), 1198.
16. He, Q., Sun, X., Yan, Z., & Fu, K. (2021). DABNet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.
17. Kechagias-Stamatis, O., & Aouf, N. (2021). Automatic target recognition on synthetic aperture radar imagery: A survey. IEEE Aerospace and Electronic Systems Magazine, 36(3), 56-81.
18. Zhang, Z., Wang, H., Xu, F., & Jin, Y. Q. (2017). Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 7177-7188.
19. Pathirana, S., Lambot, S., Krishnapillai, M., Cheema, M., Smeaton, C., & Galagedara, L. (2023). Ground-penetrating radar and electromagnetic induction: Challenges and opportunities in agriculture. Remote Sensing, 15(11), 2932.
20. Yun, S. H. A. O., & Hasi, T. (2019). Research advances of SAR remote sensing for agriculture applications: A review. Journal of integrative agriculture, 18(3), 506-525.
21. Onojeghuo, A. O., Miao, Y., & Blackburn, G. A. (2023). Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery. Remote Sensing, 15(6), 1517.
22. Kumar, V. S., Jaganathan, M., Viswanathan, A., Umamaheswari, M., & Vignesh, J. J. E. R. C. (2023). Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model. Environmental Research Communications, 5(6), 065014.
23. Gao, W., Zong, C., Wang, M., Zhang, H., & Fang, Y. (2024). Intelligent identification of rice leaf disease based on YOLO V5-EFFICIENT. Crop Protection, 183, 106758.
24. Zhou, C., Zhou, C., Yao, L., Du, Y., Fang, X., Chen, Z., & Yin, C. (2025). An improved YOLOv5s-based method for detecting rice leaves in the field. Frontiers in Plant Science, 16, 1561018.
25. Lan, M., Liu, C., Zheng, H., Wang, Y., Cai, W., Peng, Y., ... & Tan, S. (2024). Rice-yolo: In-field rice spike detection based on improved yolov5 and drone images. Agronomy, 14(4), 836.
26. Zhao, G., Lan, Y., Zhang, Y., & Deng, J. (2025). Rice Canopy Disease and Pest Identification Based on Improved YOLOv5 and UAV Images. Sensors, 25(13), 4072.
27. Li, P., Zhou, J., Sun, H., & Zeng, J. (2025). RDRM-YOLO: A High-Accuracy and Lightweight Rice Disease Detection Model for Complex Field Environments Based on Improved YOLOv5. Agriculture, 15(5), 479.
28. Peng, D., Ding, W., & Zhen, T. (2024). A novel low light object detection method based on the YOLOv5 fusion feature enhancement. Scientific reports, 14(1), 4486.
29. Zhu, X., Liu, J., Zhou, X., Qian, S., & Yu, J. (2023). Enhanced feature Fusion structure of YOLO v5 for detecting small defects on metal surfaces. International Journal of Machine Learning and Cybernetics, 14(6), 2041-2051.
30. Chen, H., Chen, S., Xu, Z., Zhang, Z., Zhang, A., & Li, Q. (2025). RALSD-YOLO: Lightweight Maize Tassel Detection Algorithm Based on Improved YOLOv8. Remote Sensing, 17(22), 3735.