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arXiv · 2302.06698

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Ritayu Nagpal·Sam Long·Shahid Jahagirdar·Weiwei Liu·Scott Fazackerley·Ramon Lawrence·Amritpal Singh
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Abstract

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.

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Ritayu Nagpal, Sam Long, Shahid Jahagirdar, Weiwei Liu, Scott Fazackerley, Ramon Lawrence, Amritpal Singh. 2023-02-13. An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection. https://arxiv.org/abs/2302.06698

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