An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection
This provides an efficient, automated solution for tree fruit breeders to reduce bias and fatigue in trait measurements, though it is incremental as it applies an existing method to a new domain.
The researchers tackled the labor-intensive and subjective manual phenotyping of sweet cherries by developing a deep learning system using YOLOv3 for automated fruit counting, size, and color analysis, achieving 99% accuracy in detection and 90% in localization.
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.