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August 17, 2025Agriculture0 citationsOpen Access

Enhanced YOLOv11 for Wheat Head Detection in Precision Agriculture

Wheat Head Detection in Field Environments Based on an Improved YOLOv11 Model

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This research introduces an improved YOLOv11 model for wheat head detection, enhancing yield estimation and precision farming.

Achieved precision of 92.5%, recall of 91.1%, and mAP@0.5 of 95.7% on a custom wheat dataset, showing a robust performance enhancement.
Improvements include a Global Edge Information Transfer module that enhances wheat head contours through deep semantic fusion.
Incorporation of the Normalized Gaussian Wasserstein Distance as a localization loss stabilizes detection of small targets effectively.
This model provides a practical tool for accurate wheat head detection in diverse field conditions, facilitating better yield estimates.

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Zhang et al. (2025) studied this question.

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August 17, 2025Agriculture0 citationsOpen Access

Wheat Head Detection in Field Environments Based on an Improved YOLOv11 Model

Authors

This research introduces an improved YOLOv11 model for wheat head detection, enhancing yield estimation and precision farming.

Achieved precision of 92.5%, recall of 91.1%, and mAP@0.5 of 95.7% on a custom wheat dataset, showing a robust performance enhancement.
Improvements include a Global Edge Information Transfer module that enhances wheat head contours through deep semantic fusion.
Incorporation of the Normalized Gaussian Wasserstein Distance as a localization loss stabilizes detection of small targets effectively.
This model provides a practical tool for accurate wheat head detection in diverse field conditions, facilitating better yield estimates.

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View Full Paper

Zhang et al. (2025) studied this question.

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View Full Paper