A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

Abstract The accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based metho...

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Autores principales:	Xin Qi, Yanan Zhao, Yufang Huang, Yang Wang, Wei Qin, Wen Fu, Yulong Guo, Youliang Ye
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eb546ed68dd340ca8a69556009258d59
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spelling	oai:doaj.org-article:eb546ed68dd340ca8a69556009258d592021-12-02T17:12:21ZA novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram10.1038/s41598-021-92431-52045-2322https://doaj.org/article/eb546ed68dd340ca8a69556009258d592021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92431-5https://doaj.org/toc/2045-2322Abstract The accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.Xin QiYanan ZhaoYufang HuangYang WangWei QinWen FuYulong GuoYouliang YeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Qi Yanan Zhao Yufang Huang Yang Wang Wei Qin Wen Fu Yulong Guo Youliang Ye A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
description	Abstract The accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.
format	article
author	Xin Qi Yanan Zhao Yufang Huang Yang Wang Wei Qin Wen Fu Yulong Guo Youliang Ye
author_facet	Xin Qi Yanan Zhao Yufang Huang Yang Wang Wei Qin Wen Fu Yulong Guo Youliang Ye
author_sort	Xin Qi
title	A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
title_short	A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
title_full	A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
title_fullStr	A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
title_full_unstemmed	A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
title_sort	novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eb546ed68dd340ca8a69556009258d59
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A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

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