Paan_Pata: The Evergreen Heritage of Betel Leaf in Bangladesh Agriculture

The Betel Leaf Disease Dataset was systematically gathered from Khashia Punji in Sreemangal Upazila, Moulvibazar District, Bangladesh, between January 3 and January 7, 2024. Initially, we gathered the raw images and subsequently augment the entire data set for improved efficiency. This dataset consists of three distinct classes: Healthy (800), Blight (800), and Anthracnose (800), capturing various healthy and diseased conditions of betel leaves. The images were captured using a Redmi 6 smartphone in natural lighting conditions, ensuring high resolution and clarity. The original images, taken in PNG format at a resolution of 256 × 256 pixels, were later resized to 640 × 480 pixels at 72 dpi to standardize the dataset. Although the dataset does not include annotations, it serves as a valuable resource for researchers and machine learning practitioners working on plant disease detection and classification. This dataset is particularly relevant for applications in precision agriculture and automated plant pathology, where early diagnosis and leaf health monitoring can significantly improve crop management strategies.

蒌叶病害数据集（Betel Leaf Disease Dataset）于2024年1月3日至1月7日，在孟加拉国毛尔维巴扎尔县斯里曼加尔乌帕齐拉的Khashia Punji区域系统采集完成。初始阶段采集原始图像，随后对全量数据集实施数据增强操作，以提升后续模型训练效率。本数据集涵盖3个独立类别：健康蒌叶（800张）、枯斑病叶（800张）与炭疽病叶（800张），完整覆盖蒌叶的各类健康与染病状态。所有图像均采用Redmi 6智能手机在自然光照环境下拍摄，确保高分辨率与画面清晰度。原始图像以PNG格式采集，分辨率为256×256像素，后续统一调整为640×480像素、72dpi，以实现数据集标准化。尽管本数据集未附带标注信息，但仍可为从事植物病害检测与分类研究的科研人员及机器学习从业者提供极具价值的研究资源。该数据集尤其适用于精准农业与自动化植物病理学相关应用场景，早期病害诊断与叶片健康监测可显著优化作物管理策略。