Accurate pre-harvest yield estimation is fundamental to pineapple production, as it facilitates optimized harvest scheduling, informs market-responsive pricing strategies, and supports data-driven decision-making in smart agriculture. The rapid advancement of object detection algorithms, coupled with the deployment of miniaturized cameras on Unmanned Aerial Vehicle (UAV) platforms, has made high-throughput pineapple counting for precise yield estimation increasingly feasible. Nonetheless, accurate detection of pineapples in UAV imagery remains challenging due to factors such as the small size of individual fruits, significant scale variations, and complex background textures, all of which impede precise localization and identification. To address these challenges, the present study introduces a novel object detection framework, termed YOLO-Pineapple, designed for accurate pineapple detection in high-resolution color images captured by UAVs. YOLO-Pineapple enhances the baseline YOLOv8 model through several key innovations: (1) the Dynamic Interactive Task Alignment Head (DITAH) is proposed to resolve feature inconsistency and task misalignment between localization and classification branches by integrating interactive and independent features, thereby improving detection accuracy; (2) the incorporation of the Grouped Multi-Scale Convolution (GMSC) module reduces redundant feature computations while capturing richer multi-scale features, enhancing performance in cluttered and heavily occluded field environments; (3) the introduction of the Spatial and Channel Synergistic Attention (SCSA) mechanism facilitates enhanced semantic feature interaction via the combined application of multi-semantic spatial attention and channel self-attention; and (4) the integration of a sample-adaptive weighting mechanism derived from Focaler-IoU with the angle-aware distance component of SIoU culminates in a novel loss function, designated FocalerSIoU, which achieves more precise bounding box regression, particularly for small objects. Experimental evaluations demonstrate the effectiveness of the proposed YOLO-Pineapple model, which attains a mean average precision (mAP) of 94. 4%, a Recall rate of 88. 9%, and a precision of 94. 6%. The optimized YOLO-Pineapple algorithm constitutes a significant advancement in overcoming the challenges associated with pineapple detection from UAV imagery, while exhibiting promising potential for yield estimation and pineapple field management.
Xue et al. (2026) studied this question.