Watermelon Disease Dataset

The Watermelon Disease Detection Dataset is a high-quality computer vision dataset designed for machine learning-based disease classification in watermelon crops. It provides labeled images for early detection and accurate classification of watermelon diseases, helping improve agricultural productivity and food security.

Dataset Overview

• Dataset Focus: Watermelon disease classification for precision agriculture
• Number of Images: 1,155 images (preprocessing may have reduced the final count)
• Image Resolution: 1080 × 1080 pixels (after resizing)
• Classes Included:
  • Mosaic Virus
  • Anthracnose
  • Downy Mildew
  • Healthy Watermelons
• Data Collection Details:
  • Collected on June 25, 2023, in collaboration with experts
  • Sourced from the Regional Horticulture Research Station, Lebukhali, Patuakhali, Bangladesh

Key Features

• High-Quality Image Data: Optimized 1080 × 1080 resolution for deep learning applications
• Comprehensive Disease Coverage: Includes multiple watermelon diseases and healthy samples
• Ideal for AI & Machine Learning: Supports image classification, object detection, and disease prediction
• Early Disease Detection: Enables precision farming and proactive crop health management
• Real-World Agricultural Application: Collected with expert supervision for reliable annotations