Real-Farm Pomegranate Crop Dataset
Description
This dataset contains real-field images of pomegranate crops collected under natural agricultural conditions for research on automated pomegranate disease detection and classification. The images represent both healthy and diseased pomegranate crops and capture variations in plant appearance, disease symptoms, illumination, background, and field conditions. The dataset includes five categories: **Healthy Pomegranate, Alternaria, Anthracnose, Bacterial Blight, and Cercospora**. The images were collected from real farming environments and are intended to support the development and evaluation of computer vision, deep learning, image classification, object detection, and explainable artificial intelligence (XAI) approaches for pomegranate disease diagnosis. Unlike synthetically generated datasets, this collection consists of **original real-field images**, making it useful for evaluating model performance under realistic agricultural conditions. The dataset can also be used as a reference dataset for studying data augmentation, generative AI, transfer learning, and robust disease recognition in precision agriculture. This dataset is intended for academic and research purposes, including machine learning model development, benchmarking, comparative analysis, and reproducibility of pomegranate disease detection studies.