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.

本数据集为农业、机器学习与计算机视觉领域的科研人员与行业从业者提供了一套综合性研究资源，旨在借助人工智能驱动的解决方案，提升瓠瓜（bottle gourd）病害检测与生长阶段分类的精度。

本数据集于2024年12月至2025年2月期间，在贾马尔普尔（Jamalpur）查波利沙（Charpolisha）的瓠瓜田中采集，由农业专家全程指导。数据采集工作由认知人工智能与信息学研究实验室（Cognitive AI & Informatics Research Lab, CAIR Lab）提供支持，保障了图像采集的高质量与标注的精准性。

数据集结构
本数据集分为两大核心类别：

A. 瓠瓜病害数据集（Bottle Gourd Disease Dataset）
本板块包含展示瓠瓜植株各类病害症状的图像，可支持精细化分类与分析。

炭疽病（Anthracnose）
霜霉病（Downy Mildew）
虫害侵染（Pest Infestation）
营养失调（Nutrient Deficiency）
叶片干枯症（Dry Leaf Disorder）
健康叶片（Healthy Leaf）

📌 原始数据集：1639张高分辨率JPEG（.jpg）图像
📌 增强数据集：9000张增强处理后的图像

B. 瓠瓜生长阶段数据集（Bottle Gourd Growth Stage Dataset）
本板块收录了瓠瓜植株关键发育阶段的图像，可辅助生长监测与产量评估。

开花期（Flower）
未成熟瓠果（Immature Gourd）
成熟瓠果（Mature Gourd）

📌 原始数据集：442张高分辨率JPEG（.jpg）图像
📌 增强数据集：4500张增强处理后的图像

研究意义
本数据集在推动人工智能驱动的农业研究中发挥关键作用，可实现以下目标：
✅ 对瓠瓜植株病害进行早期精准检测
✅ 自动化作物监测以优化产量预测
✅ 借助精准农业实践推动可持续农业发展

本数据集提供了结构清晰、标注完善的图像资源，是开发基于计算机视觉的瓠瓜植株健康评估与作物管理解决方案的宝贵工具。