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Introduction The Chili Leaf Disease Dataset contains 1544 images of chili leaves, captured using smartphone cameras in agricultural fields across Bangladesh. The images are divided into four classes: Anthracnose, Cercospora Leaf Spot, Leaf Curl Disease, and Healthy Leaves. This dataset is designed to assist in the development of machine learning models for automated disease detection in chili plants, supporting agricultural innovation and sustainable farming practices. Dataset Overview o Number of Images: 1544 o Classes: 4 — Anthracnose, Cercospora Leaf Spot, Leaf Curl Disease, Healthy Leaves o Image Sources: Captured using smartphones with varying camera specifications: Redmi 12 (50 MP), Redmi 13 (108 MP), and Tecno Spark 8 Pro (48 MP) o Geographical Region: Bangladesh The dataset includes images with varying lighting, backgrounds, and angles to ensure diversity in field conditions. This allows the dataset to reflect real-world conditions that farmers might encounter when diagnosing. Chili Leaf Diseases Anthracnose: A fungal disease caused by Colletotrichum species, leading to dark, sunken lesions on leaves and fruits. Cercospora Leaf Spot: Caused by Cercospora capsici, this disease results in dark, circular spots on leaves, causing premature leaf drop. Leaf Curl Disease: Caused by viruses like ChiVMV and ToLCV, this disease causes leaves to curl, leading to stunted growth and reduced yield. Healthy Leaves: Includes disease-free chili leaves, serving as a baseline for comparison with diseased leaves. Data Collection The images were captured across various chili fields in Bangladesh using the smartphones listed above. These smartphones were chosen for their accessibility and image quality, reflecting conditions under which farmers typically use smartphones for agricultural tasks. Images were taken from various angles and distances, with different lighting conditions, to simulate real-world scenarios. Use Cases Mobile Applications for Farmers: Develop smartphone apps enabling farmers to take pictures of their plants and receive instant diagnoses on disease presence. Precision Agriculture: Assist farmers by providing early disease detection, reducing pesticide use, and improving crop management. Agricultural Research: Support studies in plant pathology and machine learning for improved disease diagnosis and management systems. Conclusion The dataset is publicly available through Mendeley Data and comes in four different folders class-wise containing the raw JPG images and corresponding CSV metadata files.This dataset provides a valuable resource for developing automated systems that assist farmers in Bangladesh and other regions with disease detection and crop management. By leveraging machine learning, this dataset helps reduce reliance on manual inspection, improves crop health monitoring, and supports more sustainable agricultural practices.