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

Published: 06-04-2021| Version 1 | DOI: 10.17632/s8x6jn5cvr.1
Contributors:
Hafiz Tayyab Rauf,
Muhammad Ikram Ullah Lali

Description

(1) Plant diseases are the primary cause of reduced productivity in agriculture, which results in economic losses. Guava is a big source of nutrients for humans all over the world. Guava diseases, on the other hand, harm the yield and quality of the crop. (2) For the identification and classification of plant diseases, computer vision and image processing methods have been commonly used. (3) The dataset includes an image gallery of healthy and unhealthy Guava fruits and leaves that could be used by researchers to adopt advanced computer vision techniques to protect plants from disease. Dot, Canker, Mummification, and Rust are the diseases targeted in the data sets. (4) The dataset contains 306 images of healthy and unhealthy images for both Guava fruits and leaves collectively. Each image contains 6000 * 4000 dimensions with 300 dpi resolution. (5) All images were acquired from the tropical areas of Pakistan under the supervision of Prof. Dr. Ikramullah Lali. (6) All images were annotated manually by the domain expert such as For Guava fruits and leaves; Dot (76), Canker (77), Mummification (83), and Rust (70) Note: The data labeling was manual and can be updated by automatic labeling through machine learning. In the meantime, the authors can also use the data set for the clustering problem.

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