Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incid...

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Autores principales:	Shuhan Lei, Jianbiao Luo, Xiaojun Tao, Zixuan Qiu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	yellow leaf disease of arecanut unmanned aerial vehicle machine learning multisource data fusion remote sensing quantitative monitoring Science Q
Acceso en línea:	https://doaj.org/article/2b1b6abb0b9144cd9455bdd262e703d4
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spelling	oai:doaj.org-article:2b1b6abb0b9144cd9455bdd262e703d42021-11-25T18:54:21ZRemote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors10.3390/rs132245622072-4292https://doaj.org/article/2b1b6abb0b9144cd9455bdd262e703d42021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4562https://doaj.org/toc/2072-4292Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.Shuhan LeiJianbiao LuoXiaojun TaoZixuan QiuMDPI AGarticleyellow leaf disease of arecanutunmanned aerial vehiclemachine learningmultisource data fusionremote sensing quantitative monitoringScienceQENRemote Sensing, Vol 13, Iss 4562, p 4562 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	yellow leaf disease of arecanut unmanned aerial vehicle machine learning multisource data fusion remote sensing quantitative monitoring Science Q
spellingShingle	yellow leaf disease of arecanut unmanned aerial vehicle machine learning multisource data fusion remote sensing quantitative monitoring Science Q Shuhan Lei Jianbiao Luo Xiaojun Tao Zixuan Qiu Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
description	Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
format	article
author	Shuhan Lei Jianbiao Luo Xiaojun Tao Zixuan Qiu
author_facet	Shuhan Lei Jianbiao Luo Xiaojun Tao Zixuan Qiu
author_sort	Shuhan Lei
title	Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
title_short	Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
title_full	Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
title_fullStr	Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
title_full_unstemmed	Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
title_sort	remote sensing detecting of yellow leaf disease of arecanut based on uav multisource sensors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2b1b6abb0b9144cd9455bdd262e703d4
work_keys_str_mv	AT shuhanlei remotesensingdetectingofyellowleafdiseaseofarecanutbasedonuavmultisourcesensors AT jianbiaoluo remotesensingdetectingofyellowleafdiseaseofarecanutbasedonuavmultisourcesensors AT xiaojuntao remotesensingdetectingofyellowleafdiseaseofarecanutbasedonuavmultisourcesensors AT zixuanqiu remotesensingdetectingofyellowleafdiseaseofarecanutbasedonuavmultisourcesensors
_version_	1718410576276750336

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

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