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September 30, 2025AgricultureOpen Access

LiteFocus-YOLO: An Efficient Network for Identifying Dense Tassels in Field Environments

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LiteFocus-YOLO enhances object detection accuracy in maize and wheat, suggesting improved yield estimation in dense fields.

LiteFocus-YOLO achieved an mAP50 of 97.9%, indicating strong performance in detecting maize tassels.
The model incorporates Lightweight Target-Aware Attention Module to enhance detection of small targets effectively.
Cross-Feature Fusion Module optimizes localization accuracy by addressing semantic detail loss in dense planting conditions.
LF-YOLO demonstrates high precision and low computational cost compared to existing object detection models.

Wang et al. (2025) studied this question.

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September 30, 2025AgricultureOpen Access

View Full Paper

Ask AI

Bookmark

Authors

LiteFocus-YOLO enhances object detection accuracy in maize and wheat, suggesting improved yield estimation in dense fields.

LiteFocus-YOLO achieved an mAP50 of 97.9%, indicating strong performance in detecting maize tassels.
The model incorporates Lightweight Target-Aware Attention Module to enhance detection of small targets effectively.
Cross-Feature Fusion Module optimizes localization accuracy by addressing semantic detail loss in dense planting conditions.
LF-YOLO demonstrates high precision and low computational cost compared to existing object detection models.

Wang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark