Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms

Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset gen...

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Autores principales:	Paulo FLORES, ZHANG Zhao
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	wheat lodging ratio machine learning deep learning mission height uas resnet101 Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/00a35048d7b2405ebcdad58abc6de4ff
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id	oai:doaj.org-article:00a35048d7b2405ebcdad58abc6de4ff
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spelling	oai:doaj.org-article:00a35048d7b2405ebcdad58abc6de4ff2021-11-17T07:52:11ZWheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms2096-809410.12133/j.smartag.2021.3.2.202104-SA003https://doaj.org/article/00a35048d7b2405ebcdad58abc6de4ff2021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-23.shtmlhttps://doaj.org/toc/2096-8094Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset generation consisting of plot images. Considering the manual method being inefficient, inaccurate, and subjective, this study developed a new image processing-based approach for automatically generating individual field plot datasets. Images from wheat field trials at three flight heights (15, 46, and 91 m) were collected and analyzed using machine learning (support vector machine, random forest, and K nearest neighbors) and deep learning (ResNet101, GoogLeNet, and VGG16) algorithms to test their performances on detecting levels of wheat lodging percentages: non- (0%), light (<50%), and severe (>50%) lodging. The results indicated that the images collected at 91 m (2.5 cm/pixel) flight height could yield a similar, even slightly higher, detection accuracy over the images collected at 46 m (1.2 cm/pixel) and 15 m (0.4 cm/pixel) UAS mission heights. Comparison of random forest and ResNet101 model results showed that ResNet101 resulted in more satisfactory performance (75% accuracy) with higher accuracy over random forest (71% accuracy). Thus, ResNet101 is a suitable model for wheat lodging ratio detection. This study recommends that UASs images collected at the height of about 91 m (2.5 cm/pixel resolution) coupled with ResNet101 model is a useful and efficient approach for wheat lodging ratio detection.Paulo FLORESZHANG ZhaoEditorial Office of Smart Agriculturearticlewheat lodging ratiomachine learningdeep learningmission heightuasresnet101Agriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 23-34 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	wheat lodging ratio machine learning deep learning mission height uas resnet101 Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	wheat lodging ratio machine learning deep learning mission height uas resnet101 Agriculture (General) S1-972 Technology (General) T1-995 Paulo FLORES ZHANG Zhao Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
description	Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset generation consisting of plot images. Considering the manual method being inefficient, inaccurate, and subjective, this study developed a new image processing-based approach for automatically generating individual field plot datasets. Images from wheat field trials at three flight heights (15, 46, and 91 m) were collected and analyzed using machine learning (support vector machine, random forest, and K nearest neighbors) and deep learning (ResNet101, GoogLeNet, and VGG16) algorithms to test their performances on detecting levels of wheat lodging percentages: non- (0%), light (<50%), and severe (>50%) lodging. The results indicated that the images collected at 91 m (2.5 cm/pixel) flight height could yield a similar, even slightly higher, detection accuracy over the images collected at 46 m (1.2 cm/pixel) and 15 m (0.4 cm/pixel) UAS mission heights. Comparison of random forest and ResNet101 model results showed that ResNet101 resulted in more satisfactory performance (75% accuracy) with higher accuracy over random forest (71% accuracy). Thus, ResNet101 is a suitable model for wheat lodging ratio detection. This study recommends that UASs images collected at the height of about 91 m (2.5 cm/pixel resolution) coupled with ResNet101 model is a useful and efficient approach for wheat lodging ratio detection.
format	article
author	Paulo FLORES ZHANG Zhao
author_facet	Paulo FLORES ZHANG Zhao
author_sort	Paulo FLORES
title	Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
title_short	Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
title_full	Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
title_fullStr	Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
title_full_unstemmed	Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
title_sort	wheat lodging ratio detection based on uas imagery coupled with different machine learning and deep learning algorithms
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/00a35048d7b2405ebcdad58abc6de4ff
work_keys_str_mv	AT pauloflores wheatlodgingratiodetectionbasedonuasimagerycoupledwithdifferentmachinelearninganddeeplearningalgorithms AT zhangzhao wheatlodgingratiodetectionbasedonuasimagerycoupledwithdifferentmachinelearninganddeeplearningalgorithms
_version_	1718425841840422912

Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms

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