High-Throughput Dynamic Monitoring Method of Field Maize Seedling

At present, the dynamic detection and monitoring of maize seedling mainly rely on manual observation, which is time-consuming and laborious, and only small quadrats can be selected to estimate the overall emergence situation. In this research, two kinds of data sources, the high-time-series RGB imag...

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Autores principales:	ZHANG Xiaoqing, SHAO Song, GUO Xinyu, FAN Jiangchuan
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	field maize faster r-cnn recognition counting dynamic seedling detection Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/be0680a1dce140eca3d5080c6809af0a
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id	oai:doaj.org-article:be0680a1dce140eca3d5080c6809af0a
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spelling	oai:doaj.org-article:be0680a1dce140eca3d5080c6809af0a2021-11-17T07:52:11ZHigh-Throughput Dynamic Monitoring Method of Field Maize Seedling2096-809410.12133/j.smartag.2021.3.2.202103-SA003https://doaj.org/article/be0680a1dce140eca3d5080c6809af0a2021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-88.shtmlhttps://doaj.org/toc/2096-8094At present, the dynamic detection and monitoring of maize seedling mainly rely on manual observation, which is time-consuming and laborious, and only small quadrats can be selected to estimate the overall emergence situation. In this research, two kinds of data sources, the high-time-series RGB images obtained by the plant high-throughput phenotypic platform (HTPP) and the RGB images obtained by the unmanned aerial vehicle (UAV) platform, were used to construct the image data set of maize seedling process under different light conditions. Considering the complex background and uneven illumination in the field environment, a residual unit based on the Faster R-CNN was built and ResNet50 was used as a new feature extraction network to optimize Faster R-CNN to realize the detection and counting of maize seedlings in complex field environment. Then, based on the high time series image data obtained by the HTPP, the dynamic continuous monitoring of maize seedlings of different varieties and densities was carried out, and the seedling duration and uniformity of each maize variety were evaluated and analyzed. The experimental results showed that the recognition accuracy of the proposed method was 95.67% in sunny days and 91.36% in cloudy days when it was applied to the phenotypic platform in the field. When applied to the UAV platform to monitor the emergence of maize, the recognition accuracy of sunny and cloudy days was 91.43% and 89.77% respectively. The detection accuracy of the phenotyping platform image was higher, which could meet the needs of automatic detection of maize emergence in actual application scenarios. In order to further verify the robustness and generalization of the model, HTPP was used to obtain time series data, and the dynamic emergence of maize was analyzed. The results showed that the dynamic emergence results obtained by HTPP were consistent with the manual observation results, which shows that the model proposed in this research is robust and generalizable.ZHANG XiaoqingSHAO SongGUO XinyuFAN JiangchuanEditorial Office of Smart Agriculturearticlefield maizefaster r-cnnrecognitioncountingdynamic seedling detectionAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 88-99 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	field maize faster r-cnn recognition counting dynamic seedling detection Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	field maize faster r-cnn recognition counting dynamic seedling detection Agriculture (General) S1-972 Technology (General) T1-995 ZHANG Xiaoqing SHAO Song GUO Xinyu FAN Jiangchuan High-Throughput Dynamic Monitoring Method of Field Maize Seedling
description	At present, the dynamic detection and monitoring of maize seedling mainly rely on manual observation, which is time-consuming and laborious, and only small quadrats can be selected to estimate the overall emergence situation. In this research, two kinds of data sources, the high-time-series RGB images obtained by the plant high-throughput phenotypic platform (HTPP) and the RGB images obtained by the unmanned aerial vehicle (UAV) platform, were used to construct the image data set of maize seedling process under different light conditions. Considering the complex background and uneven illumination in the field environment, a residual unit based on the Faster R-CNN was built and ResNet50 was used as a new feature extraction network to optimize Faster R-CNN to realize the detection and counting of maize seedlings in complex field environment. Then, based on the high time series image data obtained by the HTPP, the dynamic continuous monitoring of maize seedlings of different varieties and densities was carried out, and the seedling duration and uniformity of each maize variety were evaluated and analyzed. The experimental results showed that the recognition accuracy of the proposed method was 95.67% in sunny days and 91.36% in cloudy days when it was applied to the phenotypic platform in the field. When applied to the UAV platform to monitor the emergence of maize, the recognition accuracy of sunny and cloudy days was 91.43% and 89.77% respectively. The detection accuracy of the phenotyping platform image was higher, which could meet the needs of automatic detection of maize emergence in actual application scenarios. In order to further verify the robustness and generalization of the model, HTPP was used to obtain time series data, and the dynamic emergence of maize was analyzed. The results showed that the dynamic emergence results obtained by HTPP were consistent with the manual observation results, which shows that the model proposed in this research is robust and generalizable.
format	article
author	ZHANG Xiaoqing SHAO Song GUO Xinyu FAN Jiangchuan
author_facet	ZHANG Xiaoqing SHAO Song GUO Xinyu FAN Jiangchuan
author_sort	ZHANG Xiaoqing
title	High-Throughput Dynamic Monitoring Method of Field Maize Seedling
title_short	High-Throughput Dynamic Monitoring Method of Field Maize Seedling
title_full	High-Throughput Dynamic Monitoring Method of Field Maize Seedling
title_fullStr	High-Throughput Dynamic Monitoring Method of Field Maize Seedling
title_full_unstemmed	High-Throughput Dynamic Monitoring Method of Field Maize Seedling
title_sort	high-throughput dynamic monitoring method of field maize seedling
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/be0680a1dce140eca3d5080c6809af0a
work_keys_str_mv	AT zhangxiaoqing highthroughputdynamicmonitoringmethodoffieldmaizeseedling AT shaosong highthroughputdynamicmonitoringmethodoffieldmaizeseedling AT guoxinyu highthroughputdynamicmonitoringmethodoffieldmaizeseedling AT fanjiangchuan highthroughputdynamicmonitoringmethodoffieldmaizeseedling
_version_	1718425886059921408

High-Throughput Dynamic Monitoring Method of Field Maize Seedling

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