UAV RGB Image Dataset for Object Detection of Ganoderma-Affected Oil Palm Trees

This dataset provides UAV RGB image tiles and crown-level object annotations for the detection of Ganoderma-affected oil palm trees. Version 2 is a methodologically reconstructed release designed to support geographically independent object-detection benchmarking. The canonical release contains 1,352 RGB image tiles (640 × 640 pixels) derived from an August 2023 UAV orthomosaic at an approximate ground sampling distance of 0.0408 m/pixel. The dataset contains 9,400 accepted oil-palm crown annotations in COCO format, comprising 722 field-derived Ganoderma-positive annotations and 8,678 survey-negative-normal annotations. Disease status was derived from field survey records linked to palm identities; RGB-image reviewers were used to validate crown geometry rather than visually diagnose Ganoderma infection. To reduce spatial leakage, the train, validation, and test partitions were defined using geographically disjoint plantation blocks before image extraction. The final release contains 1,128 training tiles, 106 validation tiles, and 118 test tiles, with no block or anchor-palm overlap between partitions. Cross-split file-integrity checks also identified no identical image files across partitions. Bounding boxes were produced using a human-calibrated semi-automatic crown annotation workflow followed by risk-based human quality control. Of 9,439 candidate object instances, 9,400 were accepted and 39 were excluded during quality control. No image augmentation is included in the canonical public release; augmentation, when used for model development, should be applied only to the training partition after the provided geographic split has been preserved. The term “survey-negative-normal” indicates a counted palm without a matched Ganoderma-positive field record under the dataset's survey-linkage protocol and should not be interpreted as laboratory-confirmed absence of Ganoderma infection. Independently field-verified annotations for other foliar stresses are not included. The release represents one plantation environment and one selected acquisition month; these limitations should be considered when evaluating model generalization. The repository includes RGB image tiles, COCO annotations for the complete dataset and each predefined split, public object- and tile-level metadata, a dataset card, a data dictionary, and SHA-256 checksums for release-integrity verification. Operational block identifiers, persistent internal palm identifiers, and exact UTM coordinates have been excluded from the public release.本数据集提供用于检测感染灵芝（Ganoderma）病的油棕树的无人机（Unmanned Aerial Vehicle, UAV）RGB图像切片与冠级目标标注。版本2为经方法学重构的发布版本，旨在支持地理独立的目标检测基准测试。 标准发布版本包含1,352张RGB图像切片（分辨率为640×640像素），这些切片源自2023年8月获取的UAV正射影像，地面采样距离约为0.0408米每像素。本数据集包含9,400条经确认的油棕树冠目标标注，格式为COCO（Common Objects in Context）格式，其中包含722条源自实地调查的灵芝病阳性标注，以及8,678条调查阴性正常标注。病害状态源自与油棕个体标识关联的实地调查记录；RGB图像评审仅用于验证树冠几何形状，而非视觉诊断灵芝感染情况。 为减少空间数据泄露，在图像提取前，通过地理上不重叠的种植园地块来划分训练集、验证集与测试集。最终发布版本包含1,128张训练切片、106张验证切片与118张测试切片，各子集之间不存在地块或锚定油棕的重叠。跨子集文件完整性检查也未发现不同子集间存在重复的图像文件。 边界框通过经人工校准的半自动树冠标注流程生成，随后辅以基于风险等级的人工质量控制。在9,439个候选目标实例中，经质量控制后确认9,400条有效标注，剔除39条。标准公开发布版本未包含图像增强操作；若在模型开发中使用图像增强，仅可在保留给定地理划分的前提下，对训练集进行增强处理。 术语“调查阴性正常”指的是在本数据集的调查关联协议下，未匹配到灵芝病阳性实地记录的已计数油棕，不应将其解读为经实验室确认未感染灵芝病。本数据集未包含其他叶面胁迫的经独立实地验证的标注。本发布版本仅针对单一种植园环境与单一选定的成像月份；在评估模型泛化能力时，应考虑到这些局限性。 本数据集仓库包含RGB图像切片、完整数据集与各预定义子集的COCO格式标注、公开的目标级与切片级元数据、数据集卡片（dataset card）、数据字典，以及用于验证发布版本完整性的SHA-256校验和。公开发布版本中未包含运营地块标识、持久化内部油棕个体标识以及精确的UTM（Universal Transverse Mercator）坐标。