Published: 26 March 2021| Version 1 | DOI: 10.17632/tgv3zb82nd.1


Datasets were taken from Arabica coffee plantation using a camera and with the help of a plant pathologist. The images were then cropped to focus on the region of interest . Image augmentation was done with the aim of increasing the dataset size and preventing over-fitting problems during model training and validation. The images are in different folders containing annotated images of healthy and Miner


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Acquisition was done using a digital camera and with the help of a pathologist. Annotation was done manually with the help of data labeling web tool. In the pre-processing phase, elimination of image misrepresentations and noises was done to improve on image quality. The methods that were applied during data pre-processing include noise filtering and contrast stretching, which were done using high pass filters. Each image from the dataset was then checked to find out if they were of the same squared shape. The images that were not of the squared shape were cropped to get the center square part of the imageData augmentation was done on the images that were collected from the field in order to improve smaller datasets by transforming them into large datasets with an aim of solving over-fitting problems. The data augmentation techniques that were used in this work include rotation and flipping.


Chuka University, University of Embu, Jomo Kenyatta University of Agriculture and Technology


Computer Science, Agricultural Engineering, Biologicals