Evaluation of lychee winter shoot length using UAV remote sensing technology

Authors

  • Zifan Shen 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China
  • Baihan Liu 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
  • Rui Xu 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China
  • Yiwei Wang 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China
  • Heguang Sun 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China
  • Yongshun Liu 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
  • Yubin Lan 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China
  • Xiangbao Meng 6. Guangzhou Joinken Network Technology Development Co., Ltd., Guangzhou 510630, China
  • Xiaoling Deng 1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China 3. Center for International Cooperation and Disciplinary Innovation of Precision Agricultural Aviation Applied Technology (‘111 Center’), Guangzhou 510642, China 4. Guangdong Provincial Engineering Research Center for Smart Agriculture, Guangzhou 510642, China

DOI:

https://doi.org/10.25165/ijabe.v18i6.9852

Keywords:

small object detection, UAV remote sensing, computer vision, deep learning

Abstract

Lychee is an important cash crop in southern China. The excessive growth of winter shoots in the early winter season will lead to an increase in nutrient consumption, which in turn affects flower bud differentiation and fruit yield. To address the issue of low efficiency in traditional manual measurement methods, this study proposes an automated detection method using UAV remote sensing technology and an improved YOLOv8n_OBB_SEB algorithm. Through multi-dimensional optimization, this method successfully solves the issue of the small size of winter shoots, similar color to branches, and leaf occlusion in the orchard environment. The specific improvements include: using the SAHI algorithm for image slicing to assist inference to improve the recognition ability of small targets; embedding the Starblock in the StarNet model into the C2f module and replacing the original C2f module in the Backbone, which reduces the number of parameters and strengthens the feature extraction ability; replacing the Concat module in the Neck part with the BiFPN structure to optimize multi-scale feature fusion; introducing the EMA attention mechanism and embedding it into the C2f module in the Neck part to achieve pixel-level attention allocation and enhance the distinguishability between the target and the background. The experimental results show that on the lychee winter shoot test set, the detection accuracy of the improved YOLOv8_OBB_SEB algorithm reaches 89.2%, which is 20.7% higher than that of the original YOLOv8_OBB algorithm. Compared with other mainstream algorithms, YOLOv8_OBB_SEB shows stronger competitiveness and robustness. Through inference detection, the four coordinates of the target rotation box can be obtained, and the actual size can be calculated by converting the pixel height to estimate the real length of the lychee winter shoots. According to the estimation results, this paper divides the winter shoots into two groups: those requiring drug intervention and those not requiring drug intervention. The specific judgment standard is that when the length of the winter shoot exceeds 3 centimeters, it is classified into the group requiring drug intervention, and when the length of the winter shoot is less than 3 centimeters, it is classified into the group not requiring drug intervention. Remote sensing data of 24 lychee trees were collected on December 3, 2024. The spraying requirements were determined through manual field surveys, which were then compared and verified with the model inference results. Finally, it was concluded that the accuracy of the model reached 83.3%. This classification method provides reliable decision support and a clear decision-making basis for the precise management of winter shoots. Key words: small object detection; UAV remote sensing; computer vision; deep learning DOI: 10.25165/j.ijabe.20251806.9852 Citation: Shen Z F, Liu B H, Xu R, Wang Y W, Sun H G, Liu Y S, et al. Evaluation of lychee winter shoot length using UAV remote sensing technology. Int J Agric & Biol Eng, 2025; 18(6): 241–249.

References

Chen W S, Ku M L. Ethephon and kinetin reduce shoot length and increase flower bud formation in lychee. HortScience, 1988; 23(6): 1078–1078.

Koul B, Taak P. Lychee (Litchi chinensis Sonn.): Pre- and Post-harvest disease management. In: Kumar M, Kumar V, Bhalla-Sarin N, Varma A. (Ed.) Lychee disease management. Springer, 2017; pp.1-26. doi: 10.1007/978-981-10-4247-8_1.

Huang X, Zeng L, Huang H B. Lychee and longan production in China. Acta Horticulturae, 2005; 665: 27–36.

Zhao Z Q, Zheng P, Xu S T, Wu X D. Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 2019; 30(11): 3212–3232.

Lu J Q, Lin W Z, Chen P F, Lan Y B, Deng X L, Niu H Y, et al. Research on lightweight citrus flowering rate statistical model combined with anchor frame clustering optimization. Sensors, 2021; 21(23): 7929.

Vásconez J P, Vásconez I N, Moya V, Calderón-Díaz M J, Valenzuela M, Besoain X, et al. Deep learning-based classification of visual symptoms of bacterial wilt disease caused by Ralstonia solanacearum in tomato plants. Computers and Electronics in Agriculture, 2024; 227: 109617. doi: 10.1016/j. compag. 2024.109617. doi: 10.1016/j.compag.2024.109617.

Mo J W, Lan Y B, Yang D Z, Wen F, Qiu H B, Chen X, et al. Deep learning-based instance segmentation method of litchi canopy from UAV-acquired images. Remote Sensing, 2021; 13(19): 3919.

Huang W X, Huo Y, Yang S C, Liu M J, Li H, Zhang M. Detection of Laodelphax striatellus (small brown planthopper) based on improved YOLOv5. Computers and Electronics in Agriculture, 2023; 206: 107657.

Li Z K, Deng X L, Lan Y B, Liu C J, Qing J J. Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net. Computers and Electronics in Agriculture, 2024; 217: 108538.

Song C Y, Zhang F, Li J S, Xie J Y, Yang C, Zhou H, et al. Detection of maize tassels for UAV remote sensing image with an improved YOLOX model. Journal of Integrative Agriculture, 2023; 22(6): 1671–1683.

Qing J J, Deng X L, Lan Y B, Li Z K. GPT-aided diagnosis on agricultural image based on a new light YOLOPC. Computers and Electronics in Agriculture, 2023; 213: 108168.

Liang J T, Chen X, Liang C J, Long T, Tang X Y, Shi Z M, et al. A detection approach for late-autumn shoots of litchi based on unmanned aerial vehicle (UAV) remote sensing. Computers and Electronics in Agriculture, 2023; 204: 107535.

Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho K. Augmentation for small object detection. arXiv, 2019; In press. Available from: http://arxiv.org/abs/1902.07296. Accessed on [2025-05-05].

Noh J, Bae W, Lee W, Seo J, Kim G. Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 2019; pp.9725–9733. doi: 10.1109/ICCV.2019.00982.

Akyon F C, Altinuc S O, Temizel A. Slicing aided hyper inference and fine-tuning for small object detection. In: 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, 2022; pp.966–970. doi: 10.1109/ICIP46576.2022.9897990.

Zhu X K, Lyu S C, Wang X, Zhao Q. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, 2021; pp.2778–2788. doi: 10.1109/ICCVW54120.2021.00312.

Cao Y R, He Z J, Wang L J, Wang W G, Yuan Y X, Zhang D W, et al. VisDrone-DET2021: The vision meets drone object detection challenge results. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, 2021; pp.2847–2854. doi: 10.1109/ICCVW54120.2021.00319.

Cai X H, Lai Q X, Wang Y W, Wang W G, Sun Z R, Yao Y Z. Poly kernel inception network for remote sensing detection. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2024; pp.27706–27716. doi: 10.1109/CVPR52733.2024.02617.

Lim J S, Astrid M, Yoon H J, Lee S I. Small object detection using context and attention. In: 2021 IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, 2021; pp.181–186. doi: 10.1109/ICAIIC51459.2021.9415217.

Liu Q, Lyu J, Zhang C P. MAE-YOLOv8-based small object detection of green crisp plum in real complex orchard environments. Computers and Electronics in Agriculture, 2024; 226: 109458.

Ma X, Dai X Y, Bai Y, Wang Y Z, Fu Y. Rewrite the Stars. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024; pp.5694–5703. doi: 10.1109/CVPR52733.2024.00544.

Tan M X, Pang R M, Le Q V. EfficientDet: Scalable and efficient object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, pp.10778–10787. doi: 10.1109/CVPR42600.2020.10179.

Ouyang D L, He S, Zhang G Z, Luo M Z, Guo H Y, Zhan J, et al. Efficient multi-scale attention module with cross-spatial learning. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island: IEEE, 2023; pp.1–5. doi: 10.1109/ICASSP49357.2023.10096516.

Lin T Y, Dollar P, Girshick R, He K M, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp.936–944. doi: 10.1109/CVPR.2017.106.

Chen J, Mai H S, Luo L B, Chen X Q, Wu K L. Effective feature fusion network in BIFPN for small object detection. In: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, 2021; pp.699–703. doi: 10.1109/ICIP42928.2021.9506347.

Mo H H, Wei L J. Tomato yellow leaf curl virus detection based on cross-domain shared attention and enhanced BiFPN. Ecological Informatics, 2025; 85: 102912.

Zhang Z Y. Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, 1999; pp.666–673. doi: 10.1109/ICCV.1999.791289.

Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional block attention module. In: Computer Vision - ECCV 2018, Springer, 2018; 11211: 3–19.

Lau K W, Po L M, Rehman Y A U. Large separable kernel attention: Rethinking the large kernel attention design in CNN. Expert Systems with Applications, 2024; 236: 121352.

Chen G, Wang H, T Chen K, Li Z J, Song Z D, Liu Y L, et al. A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2022; 52(2): 936–953. doi: 10.1109/TSMC.2020.3005231.

Tong K, Wu Y Q. Deep learning-based detection from the perspective of small or tiny objects: A survey. Image and Vision Computing, 2022; 123: 104471.

Xie X X, Cheng G, Wang J B, Yao X W, Han J W. Oriented R-CNN for object detection. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 2021; pp.3500–3509. doi: 10.1109/ICCV48922.2021.00350.

Yang X, Yan J, Feng Z, He T. R3Det: Refined single-stage detector with feature refinement for rotating object. Proceedings of the AAAI Conference on Artificial Intelligence, 2021; 35(4): 3163–3171.

Han J, Ding J, Li J, Xia G S. Align deep features for oriented object detection. IEEE Transactions on Geoscience and Remote Sensing, 2022; 60: 1–11.

Han J M, Ding J, Xue N, Xia G S. ReDet: A rotation-equivariant detector for aerial object detection. In: 2021 IEEE/CVF Conference on Coputer Vision and Pattern Recognition (CVPR), Nashville, 2021; pp.2785–2794. doi: 10.1109/CVPR46437.2021.00281.

Downloads

Published

2025-12-26

How to Cite

Shen, Z., Liu, B., Xu, R., Wang, Y., Sun, H., Liu, Y., … Deng, X. (2025). Evaluation of lychee winter shoot length using UAV remote sensing technology. International Journal of Agricultural and Biological Engineering, 18(6), 241–249. https://doi.org/10.25165/ijabe.v18i6.9852

Issue

Vol. 18 No. 6 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).