Arabian Jasmine Leaf Condition Dataset (AJLCD-2025)
Description
The Arabian Jasmine Leaf Condition Dataset (AJLCD-2025) consists of 6,315 high-resolution images of Arabian Jasmine (Jasminum sambac) leaves, collected in 2025 from Sonargaon, Narayanganj, Bangladesh (GPS: 23.6303°N, 90.6028°E; altitude: 45.2 meters). The original images were captured using a Xiaomi M2102J20SG smartphone under natural daylight conditions, and contain full EXIF metadata such as F-stop (f/1.8), ISO speed (ISO-96), exposure time (1/120 sec), focal length (5 mm), metering mode (center-weighted average), and white balance set to auto. The raw images are in JPG format, primarily with dimensions of 6000×8000 pixels (some 4000×3000), 24-bit sRGB color space, and 72 dpi resolution. To prepare the dataset for deep learning and image classification tasks, all images were preprocessed by resizing them uniformly to 1440×1080 pixels, applying background removal to isolate leaf structures, and replacing the background with a clean white canvas to enhance clarity and model performance. The dataset includes 8 different types of leaf conditions, representing various disease conditions and stages of the Arabian Jasmine plant: Anthracnose (754 images), Chlorosis (699), Dried Leaf (916), Healthy (955), Initial Stage (726), Leaf Blight (778), Leaf Spot (733), and Pest Damage (754). This AJLCD-2025 dataset is built to support many applications like leaf disease classification using CNNs and transformer models, mobile app development for plant disease diagnosis, explainable AI (XAI) research, and smart farming solutions. It is also highly relevant for fields like Agrotechnology, where it can aid in developing advanced crop management strategies, improving plant health monitoring, and optimizing sustainable agricultural practices. The hope is that this dataset will push forward progress in precision agriculture, agro-tech innovation, and AI-driven plant pathology.