Dragon fruit & leaf Dataset from Bangladesh for Classification and Ecological Research

Description: This dataset provides a well-curated collection of annotated images of dragon fruit and its leaves, aimed at enhancing machine learning and computer vision models for detecting and classifying various health conditions and stages of the dragon fruit plant. Designed to capture a wide range of environmental conditions, this dataset offers valuable resources for researchers, agriculturists, and AI practitioners interested in plant health monitoring, disease diagnosis, and agricultural automation. The dataset represents diverse visual characteristics, including both healthy and diseased states of the plant, helping to promote advanced applications in automated disease recognition and crop management.
Dataset Content: This dataset contains 4,518 images encompassing various health conditions of dragon fruit, with a focus on bacterial, fungal, and insect-related issues, as well as physical damage and healthy samples. The images cover different conditions to offer a comprehensive view of dragon fruit plant health, from healthy leaves and fruits to specific disease manifestations. This variability supports robust image-based classification and detection tasks, facilitating automated plant health assessment and ecological monitoring.
• Bacterial Diseases: 498 images
• Bacterial Wilt (Fruits): 84 images
• Fungal Infections (Anthracnose or Stem Canker): 864 images
• Healthy Leaves: 2,242 images
• Healthy Fruits: 333 images
• Insect-Infected Fruits: 53 images
• Mealybugs and Scale Insects (Fruits): 173 images
• Sunburn Damage: 271 images
Purpose: The primary objective of this dataset is to aid in developing machine learning models that can accurately identify and classify various health conditions in dragon fruit plants. This can support efforts in disease management, crop yield optimization, and biodiversity research, benefiting fields such as agriculture, botany, and environmental science.