Multi-format open-source sweet orange leaf dataset for disease detection, classification, and analysis.

Abstract

In Bangladesh, sweet orange cultivation has been popular among fruit growers as the fruit is in demand. However, the disease of sweet oranges decreases fruit production. Research suggests that computer-aided disease diagnosis and machine learning (IML) models can improve fruit production by detecting and classifying diseases. In this line, a dataset of sweet oranges is required to diagnose the disease. Moreover, like many other fruits, sweet orange disease may vary from country to country. Therefore, in Bangladesh, a sweet orange dataset is required. Lastly, since different ML algorithms require datasets in various formats, only a few existing datasets fulfil the necessity. To fulfil the limitations, a sweet orange dataset in Bangladesh is collected. The dataset was collected in August and comprises high-quality images documenting multiple disease conditions, including Citrus Canker, Citrus Greening, Citrus Mealybugs, Die Back, Foliage Damage, Spiny Whitefly, Powdery Mildew, Shot Hole, Yellow Dragon, Yellow Leaves, and Healthy Leaf . These images provide an opportunity to apply machine learning and computer vision techniques to detect and classify diseases. This dataset aims to help researchers advance agri engineering through ML. Other sweet orange growing countries with having similar environments may find helpful information. Lastly, such experiments using our dataset will assist farmers in taking preventive measures and minimising economic losses.

Code and data availability

The paper is a Data in Brief article describing a sweet orange leaf disease image dataset (5,813 images, 11 classes, plus TXT annotations), publicly deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/f7cr74mwpj.1). This is a paper-specific public plant image/annotation dataset directly reproducing