Giulietti Nicola, Tombesi Sergio, Bedodi Michele, Sergenti Carol, Carnevale Marco, Giberti Hermes
Dipartimento di Ingegneria Industriale e dell'Informazione, Università di Pavia, Via Adolfo Ferrata 5, 27100 Pavia, Italy.
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy.
Sensors (Basel). 2025 May 20;25(10):3212. doi: 10.3390/s25103212.
Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous shape and color. Currently, manual flower counting is the only available method, but it is time-consuming and prone to errors. In this study, a novel vision-based method for automatic flower counting specifically designed for hazelnut plants () exploiting a commercial high-resolution imaging system and an image-tiling strategy to enhance small-object detection is proposed. The method is designed to be fast and scalable, requiring less than 8 s per plant for processing, in contrast to 30-60 min typically required for manual counting by human operators. A dataset of 2000 labeled frames was used to train and evaluate multiple female hazelnut flower detection models. To improve the detection of small, low-contrast flowers, a modified YOLO11x architecture was introduced by adding a P2 layer, improving the preservation of fine-grained spatial information and resulting in a precision of 0.98 and a Mean Average Precision (mAP@50-95) of 0.89. The proposed method has been validated on images collected from hazelnut groves and compared with manual counting by four experienced operators in the field, demonstrating its ability to detect small, low-contrast flowers despite occlusions and varying lighting conditions. A regression-based bias correction was applied to compensate for systematic counting deviations, further improving accuracy and reducing the mean absolute percentage error to 27.44%, a value comparable to the variability observed in manual counting. The results indicate that the system can provide a scalable and efficient alternative to traditional female flower manual counting methods, offering an automated solution tailored to the unique challenges of hazelnut yield estimation.
准确估算榛子产量对于优化资源管理和收获计划至关重要。虽然开花植株上雌花的数量是年产量的可靠指标，但由于其尺寸极小且形状和颜色不显眼，对其进行计数仍然很困难。目前，人工花计数是唯一可用的方法，但它既耗时又容易出错。在本研究中，提出了一种基于视觉的自动花计数新方法，该方法专门为榛子植株设计（），利用商业高分辨率成像系统和图像平铺策略来增强小目标检测。该方法设计得快速且可扩展，每株植物的处理时间不到8秒，相比之下，人工操作员手动计数通常需要30 - 60分钟。使用一个包含2000个标记帧的数据集来训练和评估多个雌性榛子花检测模型。为了改进对小的、低对比度花朵的检测，通过添加一个P2层引入了一种改进的YOLO11x架构，提高了细粒度空间信息的保留，精度达到0.98，平均精度均值（mAP@50 - 95）为0.89。所提出的方法已在从榛子林收集的图像上得到验证，并与四名经验丰富的现场操作员的人工计数进行了比较，证明了其在存在遮挡和光照条件变化的情况下检测小的、低对比度花朵的能力。应用基于回归的偏差校正来补偿系统计数偏差，进一步提高了准确性，并将平均绝对百分比误差降低到27.44%，该值与人工计数中观察到的变异性相当。结果表明，该系统可以为传统的雌花人工计数方法提供一种可扩展且高效的替代方案，提供一种针对榛子产量估算独特挑战量身定制的自动化解决方案。
