Agronomy 2025, 15(5), 1026; https://doi.org/10.3390/agronomy15051026 (registering DOI) - 25 Apr 2025
Abstract
Automated ripeness detection in large-scale strawberry cultivation is often challenged by complex backgrounds, significant target scale variation, and small object size. To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. This model incorporates a hybrid channel–space attention mechanism to
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Automated ripeness detection in large-scale strawberry cultivation is often challenged by complex backgrounds, significant target scale variation, and small object size. To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. This model incorporates a hybrid channel–space attention mechanism to enhance its attention to key features and to reduce interference from complex backgrounds. Furthermore, the RepNCSPELAN4_L module is devised to enhance multi-scale target representation through contextual feature aggregation. Simultaneously, a 160 × 160 small-target detection head is embedded in the feature pyramid to enhance the detection capability of small targets. It replaces the original SPPF module with the higher-performance SPPELAN module to further enhance detection accuracy. Experimental results on the self-constructed strawberry dataset SRD show that YOLOv11-HRS improves mAP@0.5 and mAP@0.5:0.95 by 3.4% and 6.3%, respectively, reduces the number of parameters by 19%, and maintains a stable inference speed compared to the baseline YOLOv11 model. This study presents an efficient and practical solution for strawberry ripeness detection in natural environments. It also provides essential technical support for advancing intelligent management in large-scale strawberry cultivation.
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(This article belongs to the Section Precision and Digital Agriculture)
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