DOI: 10.57760/sciencedb.21695
title: Spring maize root system image and deep learning recognition phenotype result dataset of 2014
title: 2014年春玉米根系图像和深度学习识别表型结果数据集

description: The root system of corn, as the underground part of plants, has a crucial impact on crop growth and yield. A deep understanding of the growth characteristics and phenotypic features of spring maize roots is of great significance for improving crop yield and optimizing agricultural management. This study aims to accurately identify and extract phenotype parameters of spring maize roots through image processing technology and deep learning algorithms, in order to reveal the growth and development laws of maize roots and provide scientific basis for intelligent agricultural management. From April to September 2014, observation experiments on the growth and development of maize roots were conducted at the large-scale farmland water control test site in Jinzhou Agricultural Meteorological Experiment Station of Shenyang Atmospheric Environment Research Institute. In situ root scanning equipment was used to non destructively scan and image plant roots, continuously monitoring and obtaining root images of maize plant growth process. Subsequently, using the self-developed iROOT-V02 root image analysis software, the obtained root images were automatically recognized and analyzed, and root phenotype parameters were automatically extracted. Through this study, the phenotypic characteristics of spring maize roots at different developmental stages were successfully obtained, and root parameters were accurately extracted. The acquisition of these root phenotype data is not only beneficial for researchers to further understand the distribution characteristics of roots, but also provides important data support for improving land surface process models. This data includes annotated images (10 images), original root images during the growth period (128 images), phenotype recognition process images (1024 images), and phenotype recognition results (32 images). The effective application of the self-developed iROOT-V02 root image analysis software has also verified the feasibility and accuracy of image processing technology and deep learning algorithms in maize root phenotype recognition, providing a new technological means for intelligent agricultural management. 
description: The root system of corn, as the underground part of plants, has a crucial impact on crop growth and yield. A deep understanding of the growth characteristics and phenotypic features of spring maize roots is of great significance for improving crop yield and optimizing agricultural management. This study aims to accurately identify and extract phenotype parameters of spring maize roots through image processing technology and deep learning algorithms, in order to reveal the growth and development laws of maize roots and provide scientific basis for intelligent agricultural management. From April to September 2014, observation experiments on the growth and development of maize roots were conducted at the large-scale farmland water control test site in Jinzhou Agricultural Meteorological Experiment Station of Shenyang Atmospheric Environment Research Institute. In situ root scanning equipment was used to non destructively scan and image plant roots, continuously monitoring and obtaining root images of maize plant growth process. Subsequently, using the self-developed iROOT-V02 root image analysis software, the obtained root images were automatically recognized and analyzed, and root phenotype parameters were automatically extracted. Through this study, the phenotypic characteristics of spring maize roots at different developmental stages were successfully obtained, and root parameters were accurately extracted. The acquisition of these root phenotype data is not only beneficial for researchers to further understand the distribution characteristics of roots, but also provides important data support for improving land surface process models. This data includes annotated images (10 images), original root images during the growth period (128 images), phenotype recognition process images (1024 images), and phenotype recognition results (32 images). The effective application of the self-developed iROOT-V02 root image analysis software has also verified the feasibility and accuracy of image processing technology and deep learning algorithms in maize root phenotype recognition, providing a new technological means for intelligent agricultural management.