Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3. A system capable of analyzing the image data for other traits such as size and color was also developed using Python. The YOLO model obtained close to 99% accuracy in object detection and counting of cherries and 90% on the Intersection over Union metric for object localization when extracting size and colour information. The model surpasses human performance and offers a significant improvement compared to manual counting.

翻译：果树育种是一项长期工作，需要对大量样本反复测量各种果实品质性状。传统上，这些性状通过人工计数果实、称重间接测量果实大小，以及利用比色卡主观将果实颜色归类至不同色级来测定。这些过程耗时昂贵，且易受评估者主观偏差和疲劳影响。深度学习的最新进展为自动化该流程提供了可能。本研究采用YOLOv3方法，实现了摄像头视野中甜樱桃果实数量的实时自动计数，并利用Python开发了能分析图像数据中果实大小、颜色等性状的系统。YOLO模型在樱桃目标检测与计数中准确率接近99%，在提取大小和颜色信息时，目标定位的交并比指标达到90%。该模型超越人工表现，相较于人工计数具有显著效率提升。