Orange Leaves Images Dataset for the Detection of Huanglongbing

Published: 24 February 2025| Version 1 | DOI: 10.17632/jgkh2jxbwt.1
Contributors:
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, Juan Antonio Obispo, Xocoyotzin Guadalupe Ávila Cruz, Liliana Montserrat Camacho Ibarra, Paula Magaldi Morales Orosco,
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Description

VALUE OF THE DATA The data presented here are derived from orange leaves showing symptoms of Huanglongbing (HLB) disease and healthy leaves. These leaves were collected from Mexico's third most important orange-producing region [1], which is particularly susceptible to disease dissemination. • The dataset can be used by researchers aiming to develop machine learning models to detect and differentiate orange trees affected by HLB disease in their early stages. • This dataset enhances the collection of data gathered by other authors and proposes a reasonable balanced number of images for each class. • The images were captured using various smartphone cameras, which introduces variability and demonstrates that the models developed with this dataset are not reliant on a specific smartphone camera. DATA DESCRIPTION The dataset includes 649 orange leaves, which can be classified into two categories: HLB and Control. The first category (HLB) includes leaves images with symptoms of HLB. This category contains 270 images. The second category (Control) comprises 379 images of leaves without symptoms of HLB from healthy trees. These images serve as a control group for the dataset. The dataset is presented in two formats: 1) Raw data with a white background and 2) processed data using image standardization to minimize the influence of the background and ensure a consistent frame of reference for comparison.

Files

Steps to reproduce

Torres-Galván, Juan Carlos, et al. (2025). Under Review The dataset includes 649 orange leaves, which can be classified into two categories: HLB and Control. The first category (HLB) includes leaves images with symptoms of HLB. This category contains 270 images. The second category (Control) comprises 379 images of leaves without symptoms of HLB from healthy trees. These images serve as a control group for the dataset. The dataset is presented in two formats: 1) Raw data with a white background and 2) processed data using image standardization to minimize the influence of the background and ensure a consistent frame of reference for comparison.

Institutions

Universidad Autonoma de San Luis Potosi

Categories

Leaf Studies, Orange

Funding

Chan Zuckerberg Initiative (United States)

2023-329644

Consejo Nacional de Humanidades, Ciencias y Tecnologías

Postdoctoral fellowship 4630373

Licence