Ash Gourd Leaf Condition Dataset (AGLCD-2025): A High-Resolution Benchmark for Multi-Class Leaf Disease Classification

The Ash Gourd Leaf Condition Dataset (AGLCD-2025) consists of 3,295 high-resolution images of Ash Gourd (Benincasa hispida) leaves, collected in 2025 from agricultural fields in Bangladesh (GPS: 23.8145°N, 90.4125°E; altitude: 26.4 meters). The original images were captured using a Xiaomi M2102J20SG smartphone under natural daylight conditions, retaining full EXIF metadata such as F-stop (f/1.8), ISO speed (ISO-100), exposure time (1/125 sec), focal length (5 mm), metering mode (center-weighted average), and white balance set to auto. The raw images are in JPG format, 24-bit sRGB color space, and 72 dpi resolution.

To prepare the dataset for deep learning and image classification tasks, all images were preprocessed through multiple stages:

Background removal to isolate leaf structures and enhance clarity.

Enhanced image folder containing contrast-adjusted, noise-reduced images for improved feature extraction.

Augmented image folder with balanced 1,000 images per class created via data augmentation techniques (rotation, scaling, flipping, etc.) to address class imbalance.

The dataset covers 7 different Ash Gourd leaf conditions, representing both healthy and diseased states of the plant:

Downy Mildew (456 images)

Dried Leaf (470 images)

Healthy Leaf (547 images)

Leaf Blight (452 images)

Soft Rot (466 images)

Yellow Hallow (424 images)

Yellowing of Leaf (480 images)

This AGLCD-2025 dataset is built to support diverse applications such as leaf disease classification using CNNs and transformer-based models, mobile app development for plant disease diagnosis, explainable AI (XAI) research, and smart farming solutions. It is particularly valuable for fields like Agrotechnology, where it can aid in developing advanced crop management strategies, improving plant health monitoring, and optimizing sustainable agricultural practices. The goal of this dataset is to accelerate progress in precision agriculture, agro-tech innovation, and AI-driven plant pathology.