BRACOT - A Brazilian Arabica Coffee Tree images dataset for instance segmentation of coffee leaves

Published: 8 January 2021| Version 1 | DOI: 10.17632/pmkbyjpf6k.1
Renato Krohling,


The dataset constructed contains images of part of Arabica coffee trees affected by biotic stresses. The images were obtained using a smartphone Galaxy S8. The images were collected in September 2019 and March 2020 in the mountain regions of Santa Maria, Marechal Floriano, state of Espirito Santo, Brazil. The photos were taken in a coffee plantation. A total of 300 images of part of Arabica coffee trees were collected, including healthy leaves and diseased leaves, affected by one or more types of biotic stresses: leaf miner, rust, brown leaf spot, and cercospora leaf spot.


Steps to reproduce

The annotations on the images were made using the tool VGG Image Annotator, abbreviated by VIA (Dutta & Zisserman, 2019). A total of 1662 instances were labeled in the 300 images of the dataset. Aim of this dataset is to make possible the open access to instance segmentation of coffee trees photos to test machine and deep learning algorithms. This dataset is a complementary material to BRACOL (Krohling, Esgario, & Ventura, 2019), which was constructed for semantic segmentation and symptom classification of coffee leaves diseases and pests. Dutta, A., & Zisserman, A. (2019). The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM International Conference on Multimedia MM '19. New York, NY, USA. Krohling, R., Esgario, J., Ventura, J.. BRACOL - a Brazilian arabica coffee leaf images dataset to identification and quantification of coffee diseases and pests. 2019. URL: doi:10.17632/YY2K5Y8MXG.1


Universidade Federal do Espirito Santo


Agricultural Science, Computer Science, Artificial Intelligence, Image Segmentation, Plant Pathology, Precision Agriculture, Deep Learning