Fruit size is crucial for growers as it influences consumer willingness to buy and the price of the fruit. Fruit size and growth along the seasons are two parameters that can lead to more precise orchard management favoring production sustainability. In this study, a Python-based computer vision system (CVS) for sizing apples directly on the tree was developed to ease fruit sizing tasks. The system is made of a consumer-grade depth camera and was tested at two distances among 17 timings throughout the season, in a Fuji apple orchard. The CVS exploited a specifically trained YOLOv5 detection algorithm, a circle detection algorithm, and a trigonometric approach based on depth information to size the fruits. Comparisons with standard-trained YOLOv5 models and with spherical objects were carried out. The algorithm showed good fruit detection and circle detection performance, with a sizing rate of 92%. Good correlations (r > 0.8) between estimated and actual fruit size were found. The sizing performance showed an overall mean error (mE) and RMSE of + 5.7 mm (9%) and 10 mm (15%). The best results of mE were always found at 1.0 m, compared to 1.5 m. Key factors for the presented methodology were: the fruit detectors customization; the HoughCircle parameters adaptability to object size, camera distance, and color; and the issue of field natural illumination. The study also highlighted the uncertainty of human operators in the reference data collection (5–6%) and the effect of random subsampling on the statistical analysis of fruit size estimation. Despite the high error values, the CVS shows potential for fruit sizing at the orchard scale. Future research will focus on improving and testing the CVS on a large scale, as well as investigating other image analysis methods and the ability to estimate fruit growth.

水果大小对种植者来说至关重要，因为它影响消费者的购买意愿和水果的价格。水果的大小和随季节的生长是两个参数，可以实现更精确的果园管理，有利于生产的可持续性。在这项研究中，开发了一种基于 Python 的计算机视觉系统 (CVS)，用于直接在树上测量苹果的尺寸，以简化水果测量任务。该系统由消费级深度相机组成，并在整个季节的 17 个时间点在富士苹果园的两个距离进行了测试。 CVS 利用经过专门训练的 YOLOv5 检测算法、圆形检测算法和基于深度信息的三角方法来确定水果的大小。与标准训练的 YOLOv5 模型和球形物体进行了比较。该算法表现出良好的水果检测和圆形检测性能，定径率达到92%。 发现估计的果实大小和实际的果实大小之间存在良好的相关性（r > 0.8）。施胶性能显示总体平均误差 (mE) 和 RMSE 分别为 + 5.7 毫米 (9%) 和 10 毫米 (15%)。与 1.5 m 相比，mE 的最佳结果始终出现在 1.0 m 处。所提出方法的关键因素是：水果探测器定制； HoughCircle参数对物体大小、相机距离和颜色的适应性；以及现场自然光照的问题。该研究还强调了参考数据收集中人类操作员的不确定性（5-6%）以及随机二次抽样对水果大小估计统计分析的影响。尽管误差值较高，但 CVS 显示出在果园规模上测定水果大小的潜力。未来的研究将集中于大规模改进和测试 CVS，以及研究其他图像分析方法和估计果实生长的能力。