- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
ZHONG Haimin, MA Xu, LI Zehua, et al. Rapid grading detection on hybrid rice bud seeds based on improved YOLOv5 model[J]. Journal of South China Agricultural University, 2023, 44(6): 960-967. DOI: 10.7671/j.issn.1001-411X.202209015
|
In order to improve the grading detection accuracy and speed of hybrid rice seed vigor.
A rapid grading detection method for hybrid rice bud seeds named YOLOv5-I model, which was an improved model based on YOLOv5, was proposed. The feature extraction ability of the target channel of YOLOv5-I model was improved by introducing the SE (Squeeze-and-excitation) attention mechanism module, and a CIoU loss function strategy was adopted to improve the convergence speed of this model.
The YOLOv5-I algorithm effectively achieved the rapid grading detection of hybrid rice bud seeds, with high detection accuracy and speed. In the test set, the average accuracy of the YOLOv5-I model was 97.52%, the average detection time of each image was 3.745 ms, and the memory space occupied by the YOLOv5-I model was small with 13.7 MB. The detection accuracy and speed of YOLOv5-I model was better than those of YOLOv5s, Faster-RCNN, YOLOv4 and SSD models.
The YOLOv5-I algorithm is better than existing algorithms, improves detection accuracy and speed, and can meet the practical requirement for grading detection of hybrid rice bud seeds.
| [1] |
李振华, 王建华. 种子活力与萌发的生理与分子机制研究进展[J]. 中国农业科学, 2015, 48(4): 646-660.
|
| [2] |
李泽华, 马旭, 李秀昊, 等. 水稻栽植机械化技术研究进展[J]. 农业机械学报, 2018, 49(5): 1-20. doi: 10.6041/j.issn.1000-1298.2018.05.001
|
| [3] |
李文彬, 于秀琪, 刘春宇, 等. 农作物种子活力检测方法研究进展[J]. 农业灾害研究, 2021, 11(6): 7-10. doi: 10.3969/j.issn.2095-3305.2021.06.004
|
| [4] |
袁俊, 郑雯, 祁亨年, 等. 种子活力光学无损检测技术研究进展[J]. 作物杂志, 2020(5): 9-16. doi: 10.16035/j.issn.1001-7283.2020.05.002
|
| [5] |
孙俊, 张林, 周鑫, 等. 采用高光谱图像深度特征检测水稻种子活力等级[J]. 农业工程学报, 2021, 37(14): 171-178. doi: 10.11975/j.issn.1002-6819.2021.14.019
|
| [6] |
PANG L, WANG L, YUAN P, et al. Feasibility study on identifying seed viability of Sophora japonica with optimized deep neural network and hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2021, 190(106426): 1-12.
|
| [7] |
YANG Y, CHEN J, HE Y, et al. Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning[J]. The Royal Society of Chemistry, 2020, 72(10): 44149-44158.
|
| [8] |
MA T, TSUCHIKAWA S, INAGAKI T. Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach[J]. Computers and Electronics in Agriculture, 2020, 177(105683): 1-9.
|
| [9] |
NIE P, ZHANG J, FENG X, et al. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning[J]. Sensors and Actuators: B: Chemical, 2019, 296(126630): 1-12.
|
| [10] |
PANG L, MEN S, YAN L, et al. Rapid vitality estimation and prediction of corn seeds based on spectra and images using deep learning and hyperspectral imaging techniques[J]. IEEE Access, 2020, 8(123026): 1-11.
|
| [11] |
张秀花, 静茂凯, 袁永伟, 等. 基于改进YOLOv3-Tiny的番茄苗分级检测[J]. 农业工程学报, 2022, 38(1): 221-229. doi: 10.11975/j.issn.1002-6819.2022.01.025
|
| [12] |
王立舒, 秦铭霞, 雷洁雅, 等. 基于改进YOLOv4-Tiny的蓝莓成熟度识别方法[J]. 农业工程学报, 2021, 37(18): 170-178. doi: 10.11975/j.issn.1002-6819.2021.18.020
|
| [13] |
宋怀波, 王云飞, 段援朝, 等. 基于YOLO v5-MDC的重度粘连小麦籽粒检测方法[J]. 农业机械学报, 2022, 53(4): 245-253. doi: 10.6041/j.issn.1000-1298.2022.04.026
|
| [14] |
骆润玫, 殷惠莉, 刘伟康, 等. 基于YOLOv5-C的复杂背景广佛手病虫害识别[J]. 华南农业大学学报, 2023, 44(1): 151-160.
|
| [15] |
殷献博, 邓小玲, 兰玉彬, 等. 基于改进YOLOX-Nano算法的柑橘梢期长势智能识别[J]. 华南农业大学学报, 2023, 44(1): 142-150.
|
| [16] |
邵延华, 张铎, 楚红雨, 等. 基于深度学习的YOLO目标检测综述[J]. 电子与信息学报, 2022, 44(10): 3697-3708.
|
| [17] |
REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
|
| [18] |
PURKAIT P, ZHAO C, ZACH C. SPP-Net: Deep absolute pose regression with synthetic views[EB/OL]. arXiv: 1712.03452. (2017-12-09)[2022-05-12]. https://arxiv.org/abs/1712.03452.
|
| [19] |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[EB/OL]. arXiv: 2004.10934. (2020-04-23)[2022-05-21]. https://arxiv.org/abs/2004.10934.
|
| [20] |
LIU W, ANGUELOV D, ERHAN D, et al. Ssd: Single shot multibox detector[C]//LEIBE B, MATAS J, SEBE N, et al. Computer vision-ECCV 2016. Cham: Springer, 2016: 21-37.
|
| [21] |
陈东培. 基于图神经网络和注意力机制的会话推荐算法研究[D]. 广州: 华南理工大学, 2021.
|
| [22] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. doi: 10.1109/TPAMI.2019.2913372
|
| [23] |
REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: A metric and a loss for bounding box regression[C]// IEEE. 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). CA: IEEE, 2019: 658-666.
|
| [24] |
ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]// EAAI. Proceedings of the 34th AAAI Conference on Artificial Intelligence. NY: EAAI, 2020: 12993-13000.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: