Maize Seed Damage Identification Method Based on Improved YOLOV8n
Year: 2025
Type: article
Abstract: The case of randomly scattered maize seeds presents the problem of complex background and high density, making detection difficult. To address this challenge, this paper proposes an improved YOLOv8n model (OBW-YOLO) for detecting damaged maize seeds. The introduction of the C2f-ODConv module enhances the model’s ability to extract damaged features, especially in complex scenarios, allowing for better capture of local information. The improved BIMFPN module optimizes the fusion of shallow and deep features, reduces detail loss, and improves detection accuracy. To accelerate model convergence and improve detection precision, the traditional bounding box loss function has been replaced by WIoU, significantly enhancing both accuracy and convergence speed. Experimental results show that the OBW-YOLO model achieves a detection accuracy of 93.6%, with [email protected] and [email protected]:0.95 reaching 97.6% and 84.8%, respectively, which represents an improvement of 2.5%, 1%, and 1.2% compared to previous models. Additionally, the number of parameters and model sizes have been reduced by 33% and 22.5%, respectively. Compared to other YOLO models, OBW-YOLO demonstrates significant advantages in both accuracy and mAP. Ablation experiments further validate the effectiveness of the improvements, and heatmap analysis shows that the improved model is more precise in capturing the damaged features of maize seeds. These improvements enable the OBW-YOLO model to achieve significant performance gains in maize seed damage detection, providing an effective solution for the automated quality inspection of maize seeds in the agricultural field.
Source: Agronomy
Institutions: Hainan University, Ministry of Agriculture and Rural Affairs
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4408423279