High throughput assessment of blueberry fruit internal bruising using deep learning models

Chenjiao Tan¹Changying Li^1*Penelope Perkins-Veazie^2*Heeduk Oh²Rui Xu¹Massimo Iorizzo²

• ¹Department of Agricultural and Biological Engineering, College of Agricultural and Life Sciences, University of Florida, Gainesville, Florida, United States
• ²Department of Horticultural Science, College of Agriculture and Life Sciences, North Carolina State University, Raleigh, North Carolina, United States

The rising costs and labor shortages have sparked interest in machine harvesting of fresh-market blueberries. A major drawback of machine harvesting is the occurrence of internal bruising, as the fruit undergoes multiple mechanical impacts during this process. Evaluating fruit internal bruising manually is a tedious and timeconsuming process. In this study, we leveraged deep learning models to rapidly quantify berry fruit internal bruising. Blueberries from 61 cultivars of soft to firm types were subjected to bruise over a three-year period from 2021-2023. Dropped berries were sliced in half along the equator and digitally photographed. The captured images were first analyzed using the YOLO detection model to identify and isolate individual fruits with bounding boxes. Then YOLO segmentation models were performed on each fruit to obtain the fruit cross-section area and the bruising area, respectively. Finally, the bruising ratio was calculated by dividing the predicted bruised area by the predicted cross-sectional area. The mean Average Precision (mAP) of the bruising segmentation model was 0.94. The correlation between the bruising ratio and ground truth was 0.69 with a mean absolute percentage error (MAPE) of 15.87%. Moreover, analysis of bruising ratios of different cultivars revealed significant variability in bruising susceptibility and the mean bruising ratio of 0.22 could be an index to differentiate the bruise-resistant and bruise-susceptible cultivars. Furthermore, the mean bruising ratio was negatively correlated with mechanical texture parameter, Young's modulus 20% Burst Strain. Overall, this study presents an effective and efficient approach with a userfriendly interface to evaluate blueberry internal bruising using deep learning models, which could facilitate the breeding of blueberry genotypes optimized for machine harvesting. The models are available at https://huggingface.co/spaces/ctan/blueberrybruisingdet.