Downy Mildew Detection in Luffa Leaves Dataset

In this dataset, a total of 3483 Luffa leaf samples were collected across various field plots in Ashulia, Dhaka, Bangladesh, during the 2024 growing season. The leaves were carefully documented and categorized into healthy and diseased groups to create a comprehensive dataset for accurate classification of Downy Mildew Disease caused by Phytophthora spp.. The distribution of leaf samples is as follows:
• Healthy Leaves: 414 samples
• Diseased Leaves: 3069 samples
• Augmented Healthy Leaves: 2483 samples
• Augmented Diseased Leaves: 17593 samples

Purpose:
The Luffa Downy Mildew Disease Detection Dataset serves a critical role in machine learning applications, particularly in image classification for automatic disease detection in Luffa plants. It can be used to train models like convolutional neural networks (CNNs) to identify and classify healthy and diseased leaves based on visual features such as leaf shape, color, and texture. The dataset includes both original and augmented images, enhancing model robustness by providing diverse samples that improve generalization in various real-world scenarios. This dataset is useful in areas like agricultural automation, early disease detection, and disease management in the Luffa farming industry. Additionally, it can be integrated with environmental data (e.g., temperature, humidity, rainfall) to improve predictive models that forecast disease risks under different climatic conditions. Ultimately, this dataset helps advance machine learning models for automated disease monitoring and management in the agricultural sector.