Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (18): 3598-3615.doi: 10.3864/j.issn.0578-1752.2025.18.003

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Detection Method of Leaf Tip in Wheat Seedling Stage Based on Improved YOLOv8s

HE HaoXu2,3(), GAO Xiang1,3(), RAO Yuan2,3, ZHANG ZiRui3,4, WU Gong5, HOU YiTing2,3, HE Ye1, LI XinYi1   

  1. 1 College of Resources and Environment, Anhui Agricultural University, Hefei 230036
    2 College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036
    3 Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036
    4 College of Engineering, Anhui Agricultural University, Hefei 230036
    5 College of Agronomy, Anhui Agricultural University, Hefei 230036
  • Received:2025-02-27 Accepted:2025-07-15 Online:2025-09-18 Published:2025-09-18
  • Contact: GAO Xiang

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Abstract:

【Objective】In precision agriculture, the detection of crop seedlings can be interfered with by factors such as soil weeds, occlusion between seedling leaves, and multi-scale datasets. Based on the object detection algorithm, this paper improved the YOLOv8s algorithm and designed the wheat leaf tip detection model YOLO-Wheat to solve problems, such as leaf occlusion of wheat seedlings in the field, interference from soil weeds, and multi-view data with multiple scales, thereby enhancing the accuracy of wheat seedling leaf detection and providing a theoretical basis for wheat seedling detection at the seedling stage in precision agriculture. 【Method】Close-up and distant images of wheat seedlings were collected respectively through mobile phone cameras and on-board RGB cameras during the emergence period to construct a crop image dataset. In the network model, a pyramid structure of multi-scale feature fusion (high-level screening-feature fusion pyramid, HS-FPN) was adopted. This structure used high-level features as weights, filters low-level feature information through the channel attention module, and combined the screened features with the high-level features. Enhancing the feature expression ability of the model could effectively solve the problem of multi-scale data. Integrate the efficient local attention (ELA) local attention mechanism in the network model was used to enable the model to focus on the leaf tip information of wheat and to suppress the interference of soil background factors of weeds. Meanwhile, the loss function of YOLOv8s (complete IoULoss, CIoULoss) was optimized, and the inner-Iou Loss auxiliary bounding box loss function was introduced to enhance the network's attention to small targets and to improve the positioning accuracy of wheat leaf tips. In terms of training strategies, transfer learning was employed. The model was pre-trained using close-up images of wheat leaf tips, and then the parameters of the model were updated and optimized using distant images. 【Result】The YOLO-Wheat model was compared with five object detection models, namely Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s, and YOLOv9s. The YOLO-Wheat model was the best in wheat leaf tip detection, with a recognition accuracy rate of 92.7% and a recall rate of 85.1%, respectively. The mean Average Precision (mAP) values were 82.9%. Compared with the Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the recognition accuracy mAP values of YOLO-Wheat have increased by 17.1%, 13.6%, 11.0%, 8.7% and 3.8% respectively; the recall rates increased by 13.1%, 6.7%, 4.5%, 1.8% and 1.3%, respectively. Compared with the Faw-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the mAP values of YOLO-Wheat have increased by 16.2%, 9.8%, 5.0%, 5.9% and 0.7%, respectively. 【Conclusion】This method could effectively solve the problem of multi-scale data, achieve precise detection of small targets at the leaf tips of wheat seedlings in complex field environments using unmanned aerial vehicle (UAV) images, and provide technical support and theoretical reference for intelligent leaf counting of wheat seedlings in complex fields.

Key words: wheat, YOLOv8s, loss function, transfer learning, drone

Fig. 1

Some images of wheat seedlings collected under natural conditions a, b, and c are close-up images of wheat seedlings taken by mobile phones, d, e, and f are distant images of wheat seedlings taken by drones"

Fig. 2

Network structure diagram of YOLO-Wheat model"

Fig. 3

HS-FPN structure"

Fig. 4

SFF module structure"

Fig. 5

ELA structure diagram"

Fig. 6

Inner-IoU"

Fig. 7

Comparison of detection results of Faster-RCNN，YOLOv5s，YOLOv7，and YOLOv9 a：Faster-RCNN；b：YOLOv9s；c：YOLOv7；d：YOLOv5s"

Fig. 8

Comparison of detection results between YOLOv8s and YOLO-Wheat"

Fig. 9

Comparison of mAP, accuracy and recall index of six models"

Table 1

Results of comparison between this research model and mainstream models"

模型 Model 准确率 Precision (%) R (%) mAP (%)
Faster-RCNN 75.6 70.7 66.7
YOLOv5s 79.1 77.1 73.1
YOLOv7 81.7 80.6 77.9
YOLOv8s 84.0 83.3 77.0
YOLOv9s 88.9 83.8 82.2
YOLO-Wheat 92.7 85.1 82.9

Table 2

The effects of data enhancement on detection results"

数据处理
Data processing		 准确率
Precision (P)
(%)		 平均精度
Average accuracy (AP)
(%)
无数据增强
Data-free enhancement		 82.6 80.8
有数据增强
Active data enhancement		 83.3 81.2

Table 3

Ablation results"

HS-FPN ELA Inner-IoU P (%) mAP (%)
× × × 84.0 78.0
√ × × 86.4 79.6
× √ × 85.3 78.8
× × √ 85.5 79.1
√ × √ 88.3 80.6
√ √ × 88.1 79.4
× √ √ 87.7 81.3
√ √ √ 92.7 82.9

Fig. 10

Detection results of the improved YOLOv8s and YOLOv9s models"

Table 4

Experimental results of different feature fusion strategies"

多尺度融合模块
Multi-scale fusion module		 AP
(%)		 mAP
(%)
FPN 83.7 79.1
BiFPN 85.0 79.2
PaFPN 85.3 79.9
HS-FPN 87.3 80.1

Fig. 11

Comparison of mAP and AP indicators of different feature fusion policies"

Table 5

Experimental results of different loss functions"

IoU类别
IoU category		 精确率
Precision (%)		 mAP
(%)
GIoU 89.5 77.1
DIoU 89.3 77.8
CIoU 88.6 75.2
Inner-IoU 90.1 79.6

Table 6

Experimental results of different attention mechanisms"

注意力机制 Attention mechanism mAP@50 (%) mAP@95 (%)
CA 68.20 63.07
SE 67.73 62.09
CBAM 66.38 62.48
ELA 71.58 63.58

Fig. 12

Comparison of different attention mechanisms mAP@50 and mAP@95"

Fig. 13

Heat map visualization of different attention mechanisms"

Fig. 14

The fitting results of the number of wheat leaf tips to the actual value"

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