DOI: 10.57760/sciencedb.j00001.00977
title: A dataset of manually observed phenological phase images of major grain, cotton, and oil crops in China
title: 我国六种粮棉油作物人工观测发育期图像数据集

description: Observation of crop phenological phase is an important basis for monitoring crop growth and evaluating crop yield. This study relies on historical image data from 642 national agricultural meteorological observation stations and observer supplementary image data to select 2891 high-quality images from tens of thousands of developmental stage images, including 661 images of rice, 853 images of wheat, 698 images of corn, 204 images of cotton, 237 images of rape, and 284 images of soybeans. Through quality control and evaluation processes such as classification, screening, expert manual discrimination, and identification, and in strict accordance with the crop phenological phase standards specified in the " Specifications of Agrometeorological Observation (Main Crop Volume)", 668 crop development period images were selected to construct a dataset, including 6 subfolders, which store key development period image data of rice, wheat, corn, cotton, rape, and soybean, totaling 668 images with a size of 2.04 GB. This dataset is classified according to agricultural biological observation objects, with their growth and development process as the main line, and physical images that can intuitively reflect the physiological structure and morphological characteristics of different growth stages of agricultural organisms as the core. It systematically displays the operational standards and technical methods for agricultural meteorological crop phenology observation. It can help observers quickly, accurately, and comprehensively learn agricultural meteorological observation techniques in a short period of time, thereby improving the overall ability, level, and quality of agricultural meteorological observation. This dataset can also provide the study of agricultural production strategies to cope with climate change. It also has important reference value for the research and training of agricultural meteorological artificial observation technology, as well as popular science education on crop phenology periods.
description: Observation of crop phenological phase is an important basis for monitoring crop growth and evaluating crop yield. This study relies on historical image data from 642 national agricultural meteorological observation stations and observer supplementary image data to select 2891 high-quality images from tens of thousands of developmental stage images, including 661 images of rice, 853 images of wheat, 698 images of corn, 204 images of cotton, 237 images of rape, and 284 images of soybeans. Through quality control and evaluation processes such as classification, screening, expert manual discrimination, and identification, and in strict accordance with the crop phenological phase standards specified in the " Specifications of Agrometeorological Observation (Main Crop Volume)", 668 crop development period images were selected to construct a dataset, including 6 subfolders, which store key development period image data of rice, wheat, corn, cotton, rape, and soybean, totaling 668 images with a size of 2.04 GB. This dataset is classified according to agricultural biological observation objects, with their growth and development process as the main line, and physical images that can intuitively reflect the physiological structure and morphological characteristics of different growth stages of agricultural organisms as the core. It systematically displays the operational standards and technical methods for agricultural meteorological crop phenology observation. It can help observers quickly, accurately, and comprehensively learn agricultural meteorological observation techniques in a short period of time, thereby improving the overall ability, level, and quality of agricultural meteorological observation. This dataset can also provide the study of agricultural production strategies to cope with climate change. It also has important reference value for the research and training of agricultural meteorological artificial observation technology, as well as popular science education on crop phenology periods.