Apple Disease Dataset
Description
The dataset used in this study consists of images of apple fruit representing different visual conditions, including healthy fruit and fruit affected by diseases. The data were collected from multiple apple orchards owned by local farmers in major apple production areas. Given the high occurrence of apple fruit diseases in these regions, consultations were conducted with farmers and agricultural experts to identify the most common disease symptoms observed in the field. The dataset comprises five visual categories, including one healthy fruit class and four major apple fruit disease classes. All images were acquired directly in orchard environments, thereby reflecting real field conditions. Image acquisition was performed across multiple orchard locations under natural lighting conditions. Images were captured using a combination of digital cameras and smartphone devices with varying camera specifications. Data collection was carried out with the assistance of agricultural experts to ensure the accuracy and reliability of the dataset. This dataset is intended to support computer vision and machine learning research focused on apple fruit disease identification.
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Data Collection Images of apple fruit were collected from multiple apple orchards located in major apple production areas. The dataset includes images of both healthy fruit and fruit affected by common apple diseases. Data collection was conducted directly in orchard environments to ensure that the images reflect real field conditions. Field Verification Consultations were carried out with local farmers and agricultural experts to identify the most frequently observed disease symptoms on apple fruit. This process ensured that the selected disease categories accurately represent conditions commonly encountered in the field. Image Acquisition Image acquisition was performed using a combination of digital cameras and smartphone devices with different camera specifications, including a DSLR camera and smartphones such as Realme 9 Pro (16 MP main camera), Apple iPhone 13 (12 MP dual camera), and Samsung Galaxy S Series (64 MP main camera). All images were captured under natural lighting conditions in orchard environments, without the use of artificial illumination. Data Validation and Categorization The collected images were reviewed with the assistance of agricultural experts to verify their accuracy. Each image was then classified into one of five categories, consisting of one healthy fruit class and four apple fruit disease classes, based on clearly observable visual symptoms. Data Preparation The final dataset was organized into separate folders corresponding to each category. All images were stored in JPG format with a consistent resolution and standardized structure, making the dataset readily usable for machine learning and computer vision applications.
Institutions
- Bina Nusantara UniversityDKI Jakarta, West Jakarta