Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Image Acquisition of Sugarcane Seed Sprouts
2.3. Making of Sugarcane Seed and Sprout Dataset
2.4. Establishment of the Sugarcane-YOLO Model for Sugarcane Seed and Sprout Identification
2.4.1. YOLOv8 Object Detection Network
2.4.2. SimAM Module
- Multi-head attention layer: The input sequence is divided into multiple heads through multiple linear maps, and each head calculates the attention weight.
- Residual connection layer: The output of the multi-head attention layer is added to the input sequence to avoid information loss.
- Forward-pass layer: The output of the residual connection layer is processed by linear transformation and activation functions and then added to the output of the residual connection layer.
- Normalization layer: The output of the forward-pass layer is normalized to speed up training and enhance model performance.
2.4.3. Space-Depth Convolution
2.4.4. E-IoU Loss Function
2.4.5. Small-Object Detection Layer
2.4.6. Sugarcane-YOLO Network Structure
- SIM-C2f module: The SimAM module was integrated into the C2f module of the YOLOv8s network, forming the SIM-C2f module to achieve a stronger image feature extraction capability.
- SPD-Conv module: The SPD-Conv module was used to replace the standard convolution in the backbone network of YOLOv8s, further enhancing the feature extraction of sugarcane seed sprouts.
- E-IoU loss function: The E-IoU loss function was used to replace the C-IoU loss function in the original YOLOv8s model, aiming to improve the accuracy of the prediction box regression.
- Detection layer adjustments: The small-object detection layer was added, and the large-object-layer output of the backbone network was removed. These adjustments refined the original model network structure to make it more suitable for the detection of small and medium-sized objects.
2.5. Test Environment and Parameter Settings
2.6. Evaluation Index
3. Results and Discussion
3.1. Comprehensive Comparison of Different Attention Mechanisms
3.2. Analysis of Ablation Test Results
3.3. Small-Object Detection Layer Improvement
3.4. Comprehensive Comparison of Different Classical Models
3.5. Real-Time Recognition Results
3.6. Discussion
4. Conclusions
- SimAM: The SimAM module was added to the C2f module within the neck network, enhancing the model’s ability to capture detailed image information.
- SPD-Conv module: The SPD-Conv module was integrated into the tail of the C2f module, boosting the efficiency of feature extraction.
- E-IoU loss function: The E-IoU loss function was utilized to speed up the model’s regression process, improving training efficiency.
- Small-object detection layer: A small-object detection layer was added while removing the large-object detection layer, optimizing the model for better recognition accuracy of smaller targets.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Chen, Y. Study on the Current Situation and Development Strategy of Sugarcane Planting in Zhanjiang City. Master’s Thesis, Guangxi University, Nanning, China, 2020. [Google Scholar]
- Liu, H.; Qu, Y.; Zeng, Z. Investigation and analysis of sugarcane mechanization in Zhanjiang Agricultural Reclamation. Mod. Agric. Equip. 2013, 6, 28–33. [Google Scholar]
- Huang, M. Study on Sugarcane production Mechanization in Guangxi. Guangxi Agric. Mech. 2014, 4–7, 10. [Google Scholar] [CrossRef]
- Lv, W.; Zhang, X.; Wang, W. Agricultural machinery purchase subsidy, agricultural production efficiency and rural labor transfer. China’s Rural Econ. 2015, 8, 22–32. [Google Scholar]
- Liu, H. Focusing on improving the service capacity and supply level of technology extension to provide strong support for the development of agricultural mechanization during the “13th Five-Year Plan”—A speech at the 2016 national conference of agricultural machinery extension station owners (abstract). Agric. Mach. Technol. Promot. 2016, 10, 9–12. [Google Scholar] [CrossRef]
- Luo, X.; Liao, J.; Hu, L.; Zang, Y.; Zhou, Z. Agricultural mechanization and sustainable development. Trans. Chin. Soc. Agric. Eng. 2016, 32, 1. [Google Scholar] [CrossRef]
- Huang, M. Application of whole process mechanization technology in sugarcane Production. Agric. South 2017, 11, 16–18. [Google Scholar] [CrossRef]
- Jiang, T.; Cui, H.; Cheng, X. A calibration strategy for vision-guided robot assembly system of large cabin. Measurement 2020, 163, 107991–108000. [Google Scholar] [CrossRef]
- Chen, J.; Ma, B.; Ji, C.; Zhang, J.; Feng, Q.; Liu, X.; Li, Y. Apple inflorescence recognition of phenology stage in complex background based on improved YOLOv7. Comput. Electron. Agric. 2023, 211, 108048. [Google Scholar] [CrossRef]
- Jing, J.; Zhang, S.; Sun, H.; Ren, R.; Cui, T. YOLO-PEM: A lightweight detection method for young “Okubo” peaches in complex orchard environments. Agronomy 2024, 14, 1757. [Google Scholar] [CrossRef]
- Abdullah, A.; Amran, G.A.; Tahmid, S.A.; Alabrah, A.; AL-Bakhrani, A.A.; Ali, A. A deep-learning-based model for the detection of diseased tomato leaves. Agronomy 2024, 14, 1593. [Google Scholar] [CrossRef]
- Márquez-Grajales, A.; Villegas-Vega, R.; Salas-Martínez, F.; Acosta-Mesa, H.-G.; Mezura-Montes, E. Characterizing drought prediction with deep learning: A literature review. MethodsX 2024, 13, 102800. [Google Scholar] [CrossRef] [PubMed]
- Buayai, P.; Yok-In, K.; Inoue, D.; Nishizaki, H.; Makino, K.; Mao, X. Supporting table grape berry thinning with deep neural network and augmented reality technologies. Comput. Electron. Agric. 2023, 213, 108194. [Google Scholar] [CrossRef]
- Huang, Y.; Wang, X.; Yin, K.; Huang, M. Design and experiment of cutting and preventing shoot of sugarcane seed based on induction counting. Trans. Chin. Soc. Agric. Eng. 2015, 31, 41–47. [Google Scholar] [CrossRef]
- Dong, Z.; Shen, D.; Wei, J.; Meng, Y.; Ye, C. Design and research of sugarcane seed transport and sugarcane node detection device. J. Guangxi Univ. (Nat. Sci. Edit.) 2017, 42, 979–989. [Google Scholar] [CrossRef]
- Li, S.; Li, X.; Zhang, K.; Li, K.; Yuan, H.; Huang, Z. Improved YOLOv3 network to improve the efficiency of real-time dynamic identification of sugarcane node. Trans. Chin. Soc. Agric. Eng. 2019, 35, 185–191. [Google Scholar] [CrossRef]
- Tang, L. Research on Sugarcane Node Recognition and Cutting Based on Convolutional Neural Network. Master’s Thesis, Agricultural University, Hefei, China, 2021. [Google Scholar]
- Yang, L.; Zhang, R.; Li, L.; Xie, X. SimAM: A simple, parameter-free attention module for convolutional neural networks. In Proceedings of the Proceedings of the 38th International Conference on Machine Learning, Virtual, 18–24 July 2021; pp. 11863–11874. [Google Scholar]
- Sunkara, R.; Luo, T. No more strided convolutions or pooling: A New CNN building block for low-resolution images and small objects. In Proceedings of the Machine Learning and Knowledge Discovery in Databases, Grenoble, France, 19–23 September 2023; pp. 443–459. [Google Scholar]
- Du, S.; Zhang, B.; Zhang, P.; Xiang, P. An improved bounding box regression loss function based on CIOU loss for multi-scale object detection. In Proceedings of the 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML), Chengdu, China, 16–18 July 2021; pp. 92–98. [Google Scholar]
- Zhang, Y.; Ren, W.; Zhang, Z.; Jia, Z.; Wang, L.; Tan, T. Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 2022, 506, 146–157. [Google Scholar] [CrossRef]
- Meng, Z.; Du, X.; Xia, J.; Ma, Z.; Zhang, T. Real-time statistical algorithm for cherry tomatoes with different ripeness based on depth information mapping. Comput. Electron. Agric. 2024, 220, 108900. [Google Scholar] [CrossRef]
- Yu, C.; Feng, J.; Zheng, Z.; Guo, J.; Hu, Y. A lightweight SOD-YOLOv5n model-based winter jujube detection and counting method deployed on Android. Comput. Electron. Agric. 2024, 218, 108701. [Google Scholar] [CrossRef]
- Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Hu, Q. ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020; pp. 11531–11539. [Google Scholar]
- Peng, S.; Jiang, W.B.; Pi, H.; Bao, H.; Zhou, X. Deep snake for real-time instance segmentation. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 24 June 2020; pp. 8530–8539. [Google Scholar]
- Lin, T.Y.; Dollar, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 936–944. [Google Scholar]
- Li, H.; Li, C.; Li, G.; Chen, L. A real-time table grape detection method based on improved YOLOv4-tiny network in complex background. Biosyst. Eng. 2021, 212, 347–359. [Google Scholar] [CrossRef]
- Woo, S.; Park, J.; Lee, J.Y.; Kweon, I.S. CBAM: Convolutional block attention module. In Proceedings of the Computer Vision—ECCV 2018, Munich, Germany, 8–14 September 2018; pp. 3–19. [Google Scholar]
- Hu, J.; Shen, L.; Sun, G.; Albanie, S. Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 42, 2011–2023. [Google Scholar] [CrossRef] [PubMed]
Methods | Precision/% | Recall/% | AP50/% | AP75/% | AP50-95/% |
---|---|---|---|---|---|
YOLOv8s | 96.55 | 98.07 | 98.69 | 78.99 | 70.29 |
YOLOv8s–CBAM | 96.87 | 98.11 | 98.92 | 79.07 | 70.39 |
YOLOv8s–SE | 96.79 | 98.2 | 98.9 | 79.09 | 70.36 |
YOLOv8s–ECA | 96.94 | 98.21 | 98.79 | 78.31 | 70.01 |
YOLOv8s–SimAM | 96.89 | 98.26 | 98.97 | 79.43 | 70.56 |
YOLOv8s–CA | 96.84 | 98.26 | 98.92 | 78.32 | 70.08 |
ID | Model | SimAM | SPD-Conv | E-IoU | P/% | R/% | AP50/% | AP75/% | AP50-95/% |
---|---|---|---|---|---|---|---|---|---|
1 | YOLOv8s | × | × | × | 96.55 | 98.07 | 98.69 | 78.99 | 70.29 |
2 | YOLOv8s | √ | × | × | 96.89 | 98.26 | 98.97 | 79.43 | 70.56 |
3 | YOLOv8s | × | √ | × | 96.81 | 98.15 | 98.85 | 78.58 | 70.29 |
4 | YOLOv8s | × | × | √ | 97.05 | 98.35 | 98.93 | 79.12 | 70.37 |
5 | YOLOv8s | √ | √ | × | 97.37 | 98.63 | 99.03 | 79.69 | 70.85 |
6 | YOLOv8s | √ | × | √ | 97.13 | 98.39 | 98.98 | 80.08 | 70.56 |
7 | YOLOv8s | × | √ | √ | 96.89 | 98.26 | 98.97 | 79.43 | 70.56 |
8 | YOLOv8s | √ | √ | √ | 97.19 | 98.89 | 99.03 | 80.38 | 71.32 |
Methods | Precision/% | Recall/% | mAP50/% | mAP75/% | mAP50-95/% | FPS |
---|---|---|---|---|---|---|
YOLOv8s | 96.55 | 98.07 | 98.69 | 78.99 | 70.29 | 303 |
Improved YOLOv8s | 97.19 | 98.89 | 99.03 | 80.38 | 71.32 | 263 |
Sugarcane-YOLO | 97.42 | 98.63 | 99.05 | 81.3 | 71.61 | 294 |
Methods | Precision/% | Recall/% | mAP50/% | mAP75/% | mAP50-95/% | FPS | Size/M |
---|---|---|---|---|---|---|---|
Faster R-CNN | 95.86 | 97.19 | 97.56 | 72.17 | 64.76 | 79 | 102 |
VGG-SSD | 96.35 | 97.74 | 98.45 | 76.51 | 68.42 | 285 | 64 |
YOLOv5 | 96.63 | 97.57 | 98.46 | 77.19 | 69.17 | 277 | 18.2 |
YOLOv6 | 96.39 | 97.93 | 98.48 | 75.94 | 68.33 | 294 | 34.3 |
YOLOv8s | 96.55 | 98.07 | 98.69 | 78.99 | 70.29 | 303 | 18.5 |
Sugarcane-YOLOv8s | 97.42 | 98.63 | 99.05 | 81.3 | 71.61 | 294 | 22.4 |
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Zhang, F.; Dong, D.; Jia, X.; Guo, J.; Yu, X. Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts. Agronomy 2024, 14, 2412. https://doi.org/10.3390/agronomy14102412
Zhang F, Dong D, Jia X, Guo J, Yu X. Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts. Agronomy. 2024; 14(10):2412. https://doi.org/10.3390/agronomy14102412
Chicago/Turabian StyleZhang, Fujie, Defeng Dong, Xiaoyi Jia, Jiawen Guo, and Xiaoning Yu. 2024. "Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts" Agronomy 14, no. 10: 2412. https://doi.org/10.3390/agronomy14102412
APA StyleZhang, F., Dong, D., Jia, X., Guo, J., & Yu, X. (2024). Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts. Agronomy, 14(10), 2412. https://doi.org/10.3390/agronomy14102412