Data for publication. CANOPIES Grape bunch and peduncle dataset

In September 2021, data of grape bunches and peduncles were collected in the vineyard of the Corsira Agricultural Cooperative Society (Aprilia, Italy) (Via Corsira (https://goo.gl/maps/UdqVTNQnJTGzRnY39), where table grapes are grown. A total of 45 sequences were recorded, with a total duration of 3,660 seconds (61 minutes) and containing a total of 103,775 images and 80,489 point clouds. The images were acquired on different days and at different times, from 9am to 5pm. They had different lighting conditions due to weather conditions, density of foliage, or time of day. The images were also taken at different distances from the bunches, ranging from 30cm to 2m. 

From the data recorded, ten sequences were selected in which the peduncles and bunches were fully or partially visible, discarding those taken from too far away to identify the peduncle. The CANOPIES Grape Bunch and Peduncle (GBPD) dataset contains a total of 810 RGB images and with their correspondent binary masks for grape bunches and peduncles.

The images were recorded using an Intel RealSense Depth Camera D435. Both 640x480 and 1280x720 resolutions were used.

The dataset was labelled using VGG Image Annotator (VIA) [1], a free software that can be used without any installation. The labelling work was divided among 8 people. Each of them was given strict guidelines on how to label the images in order to keep the labelling consistent. 

These data were used for accurate detection of table grapes and peduncles for harvesting which methods can be seen in the article [2].

 References

[1] A. Dutta, A. Gupta and A. Zisserman (2019), VGG image annotator (VIA). http://www.robots.ox.ac.uk/~vgg/software/via/

[2] Gabriel Coll-Ribes, Ivan J. Torres-Rodriguez, Antoni Grau, Edmundo Guerra, Alberto Sanfeliu (2023), Accurate detection and depth estimation of table grapes and peduncles for robot harvesting, combining monocular depth estimation and CNN methods. Computer and Electronics in Agriculture. Vol. 215, December 2023, 108362. https://doi.org/10.1016/j.compag.2023.108362