A dataset of observed phenology images for 11 crops including sorghum in China

<p indent="0mm">Change in crop phenology directly affect the accumulation, quality and yield of photosynthetic products. This study is based on the historical photos data from 642 national agrometeorological observation stations and 69 national agrometeorological experimental stations, and supplemented by photos taken by professional agrometeorological observers. We selected 1,376 high-quality photos that can objectively reflect the structure and morphological characteristics of crops from tens of thousands of photos. Through a rigorous process including historical photo data collection, missing photos supplement, screening and sorting, multiple rounds of expert identification, data quality control and quality evaluation and other links, and in strict accordance with the standard of crop phenology specified in the S<italic>pecification for Agrometeorological Observation (Volume I)</italic>, 179 photos were selected to form the dataset. The dataset is organized into 11 subfolders containing photos of key developmental stages for 11 crops: 27 photos of sorghum, 17 photos of foxtail millet, 12 photos of sweet potato, 14 photos of potato, 24 photos of peanut, 13 photos of sesame, 17 photos of sunflower, 9 photos of sugarcane, 17 photos of flax, 9 photos of sugar beet and 20 photos of tobacco, with a total size of 516.89 MB. The dataset is classified according to the characteristics of crops and their phenological phases, with their growth and development process as the main line, so as to directly reflect the morphological structure characteristics of crops at different growth stages. The dataset can comprehensively display the technical standards and methodologies of agrometeorological observation for phenological monitoring of characteristic crops. It can help agrometeorological observers to quickly grasp the manual observation techniques quickly, accurately and systematically, and improve the operational ability of agrometeorological observation and the quality of observation data. Furthermore, it can provide reliable data support for the research on automatic identification algorithm of crop phenological phase, agricultural production strategies to deal with climate change. In addition, it holds significant reference value for research and technical training in manual agrometeorological observation techniques, the cultivation and management of crops, as well as for related education and popular science curriculum development.