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Fruit traits such as cluster compactness, fruit maturity, and berry number per clusters are important to blueberry breeders and producers for making informed decisions about genotype selection related to yield traits and harvestability as well as for plant management. The goal of this study was to develop a data processing pipeline to count berries, to measure maturity, and to evaluate compactness (cluster tightness) automatically using a deep learning image segmentation method for four southern highbush blueberry cultivars (‘Emerald’, ‘Farthing’, ‘Meadowlark’, and ‘Star’). An iterative annotation strategy was developed to label images that reduced the annotation time. A Mask R-CNN model was trained and tested to detect and segment individual blueberries with respect to maturity. The mean average precision for the validation and test dataset was 78.3% and 71.6% under 0.5 intersection over union (IOU) threshold, and the corresponding mask accuracy was 90.6% and 90.4%, respectively. Linear regression of the detected berry number and the ground truth showed an  R  2  value of 0.886 with a root mean square error (RMSE) of 1.484. Analysis of the traits collected from the four cultivars indicated that ‘Star’ had the fewest berries per clusters, ‘Farthing’ had the least mature fruit in mid-April, ‘Farthing’ had the most compact clusters, and ‘Meadowlark’ had the loosest clusters. The deep learning image segmentation technique developed in this study is efficient for detecting and segmenting blueberry fruit, for extracting traits of interests related to machine harvestability, and for monitoring blueberry fruit development.

Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield