Manalagi Apple Disease Dataset

Description

Purpose : Classification and identification of Manalagi apple fruit diseases for automated detection in agricultural environments. Type of data : Image files (1024 × 1024 pixels) Data format : Joint Photographic Expert Group (JPG) Number of classes : Four (Healthy, Anthracnose, Black Pox, and Powdery Mildew) Number of images : Original Images Folder: 482 images Augmented Images Folder: 7,230 images Metadata : The dataset contains labeled images categorized into four classes of apple fruit conditions. Each image represents real-world conditions captured in orchards, including variations in lighting, background, and disease characteristics. The dataset does not include patient-based metadata but focuses on visual classification of fruit conditions. Data Acquisition : Images were captured using digital cameras and smartphones directly from apple orchards owned by local farmers in Indonesia. The data collection process was conducted under natural environmental conditions to ensure real-world variability. Data Source : Agricultural sources: Orchard location: Poncokusumo, Malang, East Java, Indonesia Source: Local farmer-owned apple orchards Applications : Apple disease detection and classification, smart farming systems, precision agriculture, computer vision, deep learning model development, and automated agricultural monitoring systems.

Steps to reproduce

1. Data Collection Collect images of Manalagi apple fruits from farmer-owned orchards in Poncokusumo, Malang, Indonesia. The dataset should include both healthy and diseased fruits captured under real orchard conditions. 2. Field Verification Consult with local farmers and agricultural experts to identify common apple fruit diseases and determine the relevant classification categories. 3. Image Acquisition Capture images using smartphone cameras under natural lighting conditions. Ensure variation in angles, distances, and backgrounds to reflect real-world scenarios. 4. Data Validation and Labeling Validate the collected images with agricultural experts and classify them into four categories: Healthy, Anthracnose, Black Pox, and Powdery Mildew. 5. Data Preprocessing Resize all images to a uniform resolution of 1024 × 1024 pixels and store them in JPG format. Organize the dataset into folders based on their respective classes. 6. Data Augmentation Apply augmentation techniques such as rotation, flipping, noise addition, brightness and saturation adjustment, and image shifting to increase data variability. 7. Final Dataset Preparation Prepare the dataset in two versions: the original dataset (482 images) and the augmented dataset (7,230 images), making it ready for machine learning and computer vision applications.

Institutions

• Bina Nusantara University

  DKI Jakarta, West Jakarta

Categories

Licence