BlueberryDCM: A Canopy Image Dataset for Detection, Counting, and Maturity Assessment of Blueberries

The BlueberryDCM dataset consists of 140 RGB images of blueberry canopies captured at varied spatial scales. All the images were acquired using smartphones in natural field light conditions in different orchards in the season of 2022, with 134 images in Mississippi and 6 images in Michigan.  A total of 17,955 bounding box annotations were manually done in the VGG Image Annotator (VIA) (v2.0.12) for the blueberry instances of two fruit maturity classes, "Blue" and "Unblue", representing ripe and unripe fruit, respectively. In addition, for each maturity class, there are two sub-categories in the annotation, "visible", and "occluded", to indicate whether the fruit is fully visible in the canopy or partially occluded. The original annotation format exported from the VGG is VIA .json.  The derived annotation files in two other formats, .xml (Pascal VOC format) and .txt (YOLO format with noralized xywh, with 0, 1, 2,  and 3 denoting the four categories of "Unblue_visible", "Unblue_occluded", "Blue_visible", and "Blue_occluded" bluerries, respectively) are provided in the dataset for the compatibility of a wide range of object detectors. Hence, the dataset contains both the raw images (.jpg) and three corresponding annotations files (.json, .xml, and .txt) with the same file names, totaling about 107 MB in file size.  

The dataset was used for in a study (see below) on the evaluation of YOLOv8 and YOLOv9 models for blueberry detection, counting, and maturity assessment. The detection accuracy of 93% mAP@50 was achieved by YOLOv8l, with an error of about 10 blueberries in fruit counting and an error of 3.6% in estimating the "Blue" fruit percentage. Software programs for the modeling work are made publicly available at: https://github.com/vicdxxx/BlueberryDetectionAndCounting. In addition, the blueberry dataset was also used as a preliminary database for developing an iOS-based mobile application, which is described in Deng, B., Lu, Y., WanderWeide, J., 2024. Development and preliminary evaluation of a deep learning-based fruit counting mobile application for highbush Blueberries. 2024 ASABE Annual International Meeting 2401022

Details about the dataset curation and statistics as well as modeling experiments are described in the journal article: Deng, B., Lu, Y., 2024.  Detection, Counting, and Maturity Assessment of Blueberries in Canopy Images using YOLOv8 and YOLOv9. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2024.100620. If you use the dataset in published research, please consider citing the dataset or the journal article. Hopefully, you find the dataset useful.