辽北苹果叶片氮含量、近红外光谱与图像数据集

氮素对苹果树的生长发育、苹果的营养及产量等都有着非常重要的作用。近红外光谱作为一种无损检测手段有着方便、快速等方面优势。随着光谱技术和图像处理技术的发展，利用光谱和图像分析等技术可构建植物的生化组分预测模型，从而达到快速无损检测的目的。然而目前多数研究仅获取了苹果叶片近红外光谱数据、矿质元素数据和图像数据的一种或两种，同时测定近红外光谱数据、氮元素及图像数据的数据集较少，因此，构建叶片光谱、图像和矿质元素数据集具有再次开发利用价值，支持科研发现。本研究通过收集辽北地区国家苹果资源圃中4种苹果树以及4个不同树龄“寒富”苹果树的健康标准果树叶片，对叶片进行近红外光谱数据、高清图像和氮含量的联合收集工作，建立苹果树标准叶片近红外光谱、标准图像和氮含量的数据集，以期为使用无损手段测定苹果叶片营养诊断提供数据支撑，并为今后利用高空遥感技术开展精准果业生产提供基础数据。

Nitrogen plays a crucial role in the growth and development of apple trees, as well as the nutrition and yield of apples. Near-infrared spectroscopy (NIRS), as a non-destructive detection method, boasts advantages including convenience and rapidity. With the advancement of spectroscopy and image processing technologies, techniques such as spectral and image analysis can be employed to construct predictive models for plant biochemical components, thereby achieving the goal of rapid non-destructive detection. However, most current studies only collect one or two types of data among the near-infrared spectral data, mineral element data and image data of apple leaves, while datasets that simultaneously measure near-infrared spectral data, nitrogen content and image data are relatively scarce. Therefore, the constructed dataset of leaf spectral, image and mineral element data has reutilization value and supports scientific research discoveries. In this study, healthy standard leaves were collected from four apple tree varieties and four "Hanfu" apple trees of different tree ages in the National Apple Germplasm Repository in northern Liaoning. Joint collection of near-infrared spectral data, high-definition images and nitrogen content of the leaves was conducted, and a dataset of near-infrared spectra, standard images and nitrogen content of apple tree standard leaves was established. This dataset is intended to provide data support for non-destructive determination-based nutritional diagnosis of apple leaves, and offer basic data for future precision fruit industry production using high-altitude remote sensing technologies.