Frontiers in plant science · 2026
DCC-YOLOv8n: a lightweight model for maize seedling and weed recognition in complex farmland environments.
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Cui Jiapeng, Hao Shengqiang, Yang Yinyin, Chen Zhuo, Bai Nan
Аннотация
INTRODUCTION: To achieve high-precision and high-efficiency recognition of maize seedlings and weeds in complex field environments while meeting the deployment requirements of resource-constrained edge devices, this study constructed a lightweight object detection model, DCC-YOLOv8n.
METHODS: Based on YOLOv8n, the model incorporates three key improvements: a dynamic convolution module to enhance feature diversity, a context-guided module to improve target identification in complex backgrounds, and a content-aware reassembly of features (CARAFE) module to improve small-object detection. A self-built dataset of maize seedlings and weeds containing 1,200 images was utilized, incorporating multidimensional data augmentation.
RESULTS: Experimental results demonstrated that DCC-YOLOv8n achieved a precision of 90.1% and a recall of 95.5%, with mAP@0.5 and mAP@0.5:0.95 reaching 93.7% and 73.9%, respectively, outperforming YOLOv8n and mainstream comparison models, including Faster R-CNN and YOLOv5n. Deployment on the NVIDIA Jetson Nano edge-computing platform achieved a real-time inference speed of 18.6 FPS with mAP@0.5 of 90.8%, verifying the feasibility of its application and deployment on actual farmland edge devices.
DISCUSSION: The proposed DCC-YOLOv8n model achieved an optimal balance between accuracy, lightweight architecture, and real-time performance. The constructed field recognition system effectively addresses the challenges of complex farmland scenarios and provides a viable technical solution for intelligent operations in precision agriculture.
METHODS: Based on YOLOv8n, the model incorporates three key improvements: a dynamic convolution module to enhance feature diversity, a context-guided module to improve target identification in complex backgrounds, and a content-aware reassembly of features (CARAFE) module to improve small-object detection. A self-built dataset of maize seedlings and weeds containing 1,200 images was utilized, incorporating multidimensional data augmentation.
RESULTS: Experimental results demonstrated that DCC-YOLOv8n achieved a precision of 90.1% and a recall of 95.5%, with mAP@0.5 and mAP@0.5:0.95 reaching 93.7% and 73.9%, respectively, outperforming YOLOv8n and mainstream comparison models, including Faster R-CNN and YOLOv5n. Deployment on the NVIDIA Jetson Nano edge-computing platform achieved a real-time inference speed of 18.6 FPS with mAP@0.5 of 90.8%, verifying the feasibility of its application and deployment on actual farmland edge devices.
DISCUSSION: The proposed DCC-YOLOv8n model achieved an optimal balance between accuracy, lightweight architecture, and real-time performance. The constructed field recognition system effectively addresses the challenges of complex farmland scenarios and provides a viable technical solution for intelligent operations in precision agriculture.