大田作物大多采取露地种植方式。东北地区全年温度较低，在作物苗期如果出现日照和降雨量大范围的波动，则十分容易导致农作物出现苗弱苗小、根系长势弱以及发育不全和生长缓慢等现象。若能对农作物苗期实时监测和管理，及时掌握其生长状态及其环境情况，便可及早做出决策。本研究于2022年5月9日—2022年6月16日期间，对试验田内11个气象站的小麦、玉米和水稻、小麦苗期图像进行采集，通过整理和筛查后形成的数据量约为2.59 GB，其中可见光RGB 1.48 GB，近红外光谱 1.11 GB。本数据集可以通过RGB可见光数据和近红外光谱数据完成对作物的叶龄识别，将提取出的特征（颜色特征、图像特征、纹理特征、植被指数）带入机器学习回归模型中进行分析预测，同时本数据集还适用于构建作物识别或幼苗识别的卷积神经网络模型，以进一步精准实现作物检测及插秧后漏苗、补苗等研究。

During the cultivation process, most field crops are typically grown in open fields. The northeastern region of China experiences relatively low temperatures throughout the year. During the seedling stage of crops, significant fluctuations in sunlight and rainfall can easily lead to issues such as weak and stunted seedlings, poorly developed root systems, and slow growth. Timely monitoring and management of crops during the seedling stage can help in understanding their growth status and environmental conditions, enabling prompt decision-making.Experimental data was collected from May 9, 2022, to June 16, 2022. RGB cameras installed at 11 meteorological stations in the experimental fields collected data seven times a day at 6:00, 8:00, 10:00, 12:00, 14:00, 16:00, and 18:00. The images were captured at a height of 2.4 meters with a field of view angle of 90°, covering an area of 4.4 meters in length and 2.5 meters in width. Photography was mainly conducted through natural light conditions with a downward vertical perspective.After organizing and screening, the dataset comprises approximately 2.59 GB of data, including 1.48 GB of visible light RGB data and 1.11 GB of near-infrared spectral data. This dataset enables leaf age identification through visible light RGB data and near-infrared spectral data. Extracted features (color features, image features, texture features, vegetation indices) can be inputted into machine learning regression models for analysis and prediction. Moreover, this dataset is suitable for constructing convolutional neural network models for crop recognition or seedling identification, facilitating precise crop detection and further research on issues such as missed or replanted seedlings after transplanting.