Publication Date:
2019
Description:
〈p〉Publication date: Available online 19 August 2019〈/p〉
〈p〉〈b〉Source:〈/b〉 Data in Brief〈/p〉
〈p〉Author(s): Jorge Parraga-Alava, Kevin Cusme, Angélica Loor, Esneider Santander〈/p〉
〈div xml:lang="en"〉
〈h5〉Abstract〈/h5〉
〈div〉〈p〉In this article we introduce a 〈em〉robusta〈/em〉 coffee leaf images dataset called RoCoLe. The dataset contains 1560 leaf images with visible red mites and spots (denoting coffee leaf rust presence) for infection cases and images without such structures for healthy cases. In addition, the data set includes annotations regarding objects (leaves), state (healthy and unhealthy) and the severity of disease (leaf area with spots). Images were all obtained in real-world conditions in the same coffee plants field using a smartphone camera. RoCoLe data set facilitates the evaluation of the performance of machine learning algorithms used in image segmentation and classification problems related to plant diseases recognition. The current dataset is freely and publicly available at 〈a href="https://doi.org/10.17632/c5yvn32dzg.2" target="_blank"〉https://doi.org/10.17632/c5yvn32dzg.2〈/a〉.〈/p〉〈/div〉
〈/div〉
Print ISSN:
2352-3409
Topics:
Biology
