Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB

• Published in: Frontiers in plant science Vol. 16; p. 1558378
• Main Authors: Shi, Hang, Liu, Changxi, Wu, Miao, Zhang, Hui, Song, Hang, Sun, Hao, Li, Yufei, Hu, Jun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03.04.2025
• Subjects: average temperature weight; Chinese cabbage seedlings; deep learning; Plant Science; real-time detection; YOLO11-CGB; Chinese cabbage seedlings; deep learning; YOLO11-CGB; real-time detection; average temperature weight
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2025.1558378

Accurate application of pesticides at the seedling stage is the key to effective control of Chinese cabbage pests and diseases, which necessitates rapid and accurate detection of the seedlings. However, the similarity between the characteristics of Chinese cabbage seedlings and some weeds is a great challenge for accurate detection. This study introduces an enhanced detection method for Chinese cabbage seedlings, employing a modified version of YOLO11n, termed YOLO11-CGB. The YOLO11n framework has been augmented by integrating a Convolutional Attention Module (CBAM) into its backbone network. This module focuses on the distinctive features of Chinese cabbage seedlings. Additionally, a simplified Bidirectional Feature Pyramid Network (BiFPN) is incorporated into the neck network to bolster feature fusion efficiency. This synergy between CBAM and BiFPN markedly elevates the model's accuracy in identifying Chinese cabbage seedlings, particularly for distant subjects in wide-angle imagery. To mitigate the increased computational load from these enhancements, the network's convolution module has been replaced with a more efficient GhostConv. This change, in conjunction with the simplified neck network, effectively reduces the model's size and computational requirements. The model's outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map's effectiveness. Comparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. The model achieves precision, recall, and mean Average Precision of 94.7%, 93.0%, and 97.0%, respectively, significantly reducing false negatives and false positives. With a file size of 3.2 MB, 4.1 GFLOPs, and a frame rate of 143 FPS, YOLO11-CGB model is designed to meet the operational demands of edge devices, offering a robust solution for precision spraying technology in agriculture.