Dataset for Detecting Anthracnose in Hog Plum and Bottle Gourd Leaves.

Tittle: Agricultural Disease Management: Dataset for Detecting Anthracnose in Hog Plum and Bottle Gourd Leaves.
In this dataset, a total of 4883 leaf samples were collected from two crop species—hog plum (Spondias mombin) and bottle gourd (Lagenaria siceraria)—sourced from various locations in Bangladesh, including agricultural fields, grocery stores, and other sources. The leaves were carefully documented and categorized into healthy and diseased groups to create a comprehensive dataset for accurate classification of anthracnose. The distribution of leaf samples is as follows:
Bottle Gourd: 15603 samples
Original: 3323 samples
Healthy Leaves: 915 samples
Diseased Leaves: 1720 samples
Extreme Diseased Leaves: 688 samples
Augmented: 12280 samples
Healthy Leaves: 3659 samples
Diseased Leaves: 5718 samples
Extreme Diseased Leaves: 2903 samples

Collection Sites: 1,833 samples from Ashulia Model Town, Ashulia, Dhaka, Bangladesh, and 1,500 from Baroghoria, Chapai Nawabganj, Rajshahi, Bangladesh.

Hog Plum: 8302 samples
Original: 1560 samples
Healthy Leaves: 545 samples
Diseased Leaves: 798 samples
Torn Diseased Leaves: 217 samples
Augmented: 6742 samples
Healthy Leaves: 2397 samples
Diseased Leaves: 3313 samples
Torn Diseased Leaves: 1032 samples

Collection Site: Ashulia Model Town, Ashulia, Dhaka, Bangladesh.

Purpose
The Dataset of Anthracnose in Hog Plum and Bottle Gourd Leaves serves a critical role in deep learning applications, particularly in image classification for automatic disease detection. It can be used to train models like Convolutional Neural Networks (CNNs) to identify and classify anthracnose based on visual features such as leaf shape, color, and texture. This dataset is useful in areas like agricultural supply chain automation, real-time disease identification in farming settings, and crop quality control. Additionally, it can be integrated with environmental data (e.g., temperature, humidity, rainfall) to enhance predictive models that forecast disease risks under varying climatic conditions. Ultimately, this dataset helps advance machine learning models for automated disease detection, crop health monitoring, and agricultural productivity improvements, making a significant impact on the agriculture industry and beyond.