Tan Chenjiao, Li Changying, Perkins-Veazie Penelope, Oh Heeduk, Xu Rui, Iorizzo Massimo
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States.
Department of Horticultural Science, North Carolina State University, Raleigh, NC, United States.
Front Plant Sci. 2025 May 21;16:1575038. doi: 10.3389/fpls.2025.1575038. eCollection 2025.
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 time-consuming 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 user-friendly 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/c-tan/blueberrybruisingdet.
成本上升和劳动力短缺引发了人们对鲜食蓝莓机械采收的兴趣。机械采收的一个主要缺点是果实内部会出现瘀伤，因为在这个过程中果实会受到多次机械冲击。手动评估果实内部瘀伤是一个繁琐且耗时的过程。在本研究中，我们利用深度学习模型快速量化蓝莓果实内部瘀伤情况。在2021年至2023年的三年时间里，对61个从软到硬不同类型品种的蓝莓进行瘀伤处理。将掉落的蓝莓沿赤道切成两半并进行数码拍照。首先使用YOLO检测模型对捕获的图像进行分析，以识别并通过边界框分离出单个果实。然后对每个果实执行YOLO分割模型，分别获得果实横截面积和瘀伤面积。最后，通过将预测的瘀伤面积除以预测的横截面积来计算瘀伤率。瘀伤分割模型的平均平均精度（mAP）为0.94。瘀伤率与实际情况之间的相关性为0.69，平均绝对百分比误差（MAPE）为15.87%。此外，对不同品种瘀伤率的分析表明，不同品种在瘀伤易感性方面存在显著差异，平均瘀伤率0.22可作为区分抗瘀伤和易瘀伤品种的指标。此外，平均瘀伤率与机械质地参数杨氏模量20%破裂应变呈负相关。总体而言，本研究提出了一种有效且高效的方法，通过用户友好的界面利用深度学习模型评估蓝莓内部瘀伤情况，这有助于培育适合机械采收的蓝莓基因型。这些模型可在https://huggingface.co/spaces/c-tan/blueberrybruisingdet获取。
