High-throughput phenotyping tools for blueberry count, weight, and size estimation based on modified YOLOv5s

The increasing popularity of blueberry has led to expanded blueberry production in many parts of the world. Berry size and average berry weight are key factors in determining the price and marketability of blueberries and therefore are important traits for breeders and researchers to evaluate. Manual measurement of berry size and average berry weight is labor-intensive and prone to human error. This study developed an automated algorithm and smartphone application for accurate blueberry count and size estimation. Two different computer vision pipelines based on traditional methods and deep neural networks were implemented to detect and segment individual blueberries from Red-Green-Blue (RGB) images. The first pipeline used traditional algorithms such as Hough Transform, Watershed, and filtering. The second pipeline deployed YOLOv5 models with additional modifications using the Ghost module and bi-Feature Pyramid Network (biFPN). A total of 198 images of blueberries, together with manually measured berry count and average berry weight, were used to train and test the model performance. The YOLOv5-based model miscounted four berries in 4,604 total berries across the 198 images. The mean average precision was 92.3%, averaged across an intersection-over-union threshold between 0.50−0.95. The model-derived average berry size was highly correlated with measured average berry weight (<i>R</i>² &gt; 0.93), which translated to a mean absolute error of around 0.14 g (8.3%). An Android application was also developed in this study to allow easier access to implemented models for berry size and weight phenotyping.