Abstract:

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.

Key words: Heilongjiang, seedling stage dataset, wheat, corn, rice, visible light image, near-infrared spectral image