A Citrus Fruits and Leaves Dataset for Detection and Classification of Citrus Diseases through Machine Learning

Published: 28-05-2019| Version 2 | DOI: 10.17632/3f83gxmv57.2
Hafiz Tayyab Rauf,
Basharat ALi Saleem,
M. Ikram Ullah Lali,
attique khan,
Muhammad Sharif,
Syed Ahmad Chan Bukhari


(1) In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. (2) The computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. (3) The dataset contains an image gallery of healthy and unhealthy citrus fruits and leaves that could be usable for the researchers to prevent plants from diseases using advanced computer vision techniques. The disease targeted in the data sets are the Blackspot, Canker, Scab, Greening, and Melanose. (4) The dataset contains 759 images of healthy and unhealthy images for both Citrus fruits and leaves collectively. Each image contains 256 * 25 dimensions with 72 dpi resolution. (5) All images were acquired from the Sargodha region, a tropical area of Pakistan under the supervision of Dr. Basharat ALi Saleem, Endeavour Executive Fellow Curtin University · Horticulture Research Laboratory Postharvest Australia · Bentley (6) All images were annotated manually by the domain expert Dr. Basharat ALi Saleem to represent their every class such as : For Citrus fruits (Black Spot, Canker, Greening, Scab, and healthy with total number of 150 images ), For Citrus Leaves (Black Spot, Canker, Greening, Melanose, and healthy with total number of 609 image) (6) Further details can be found in the associated publications with the dataset.