CAIR-BGD-2025: Annotated Dataset for Bottle Gourd Disease & Growth Stages

This dataset is a comprehensive resource for researchers and professionals in agriculture, machine learning, and computer vision. It is designed to enhance the detection and classification of bottle gourd plant diseases and growth stages using AI-driven solutions.

The dataset was collected from bottle gourd fields in Charpolisha, Jamalpur, between December 2024 and February 2025, under the supervision of an agricultural expert. The data collection process was supported by the Cognitive AI & Informatics Research Lab (CAIR Lab), ensuring high-quality image acquisition and precise annotation.

Dataset Structure
The dataset is divided into two primary categories:

A. Bottle Gourd Disease Dataset
This section contains images illustrating various disease conditions affecting bottle gourd plants, ensuring detailed classification and analysis.

Anthracnose
Downy Mildew
Pest Infestation
Nutrient Deficiency
Dry Leaf Disorder
Healthy Leaf
📌 Original Dataset: 1,639 high-resolution images (.jpg)
📌 Augmented Dataset: 9,000 enhanced images

B. Bottle Gourd Growth Stage Dataset
This section captures key developmental stages of bottle gourd plants, assisting in growth monitoring and yield assessment.

Flower
Immature Gourd
Mature Gourd
📌 Original Dataset: 442 high-resolution images (.jpg)
📌 Augmented Dataset: 4,500 enhanced images

Significance
This dataset plays a crucial role in advancing AI-driven agricultural research by enabling:

✅ Early and accurate disease detection in bottle gourd plants
✅ Automated crop monitoring for enhanced yield prediction
✅ Sustainable farming practices through precision agriculture

This dataset offers well-structured, annotated images, making it a valuable tool for developing computer vision-based solutions in bottle gourd plant health assessment and crop management.