Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network

Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed typ...

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Autores principales:	Jing Zhang, Jerome Maleski, David Jespersen, F. C. Waltz, Glen Rains, Brian Schwartz
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Bermudagrass artificial intelligence Fastai ResNet RGB imagery Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/daf9bf860603469f842428ae02c0ee6f
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id	oai:doaj.org-article:daf9bf860603469f842428ae02c0ee6f
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spelling	oai:doaj.org-article:daf9bf860603469f842428ae02c0ee6f2021-12-01T05:33:43ZUnmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network1664-462X10.3389/fpls.2021.702626https://doaj.org/article/daf9bf860603469f842428ae02c0ee6f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.702626/fullhttps://doaj.org/toc/1664-462XWeeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm–1.28 cm pixel–1 resolution. Recall ranges for the five classes were 0.78–0.93, 0.65–0.87, 0.82–0.93, 0.52–0.79, and 0.94–0.99. Additionally, this study demonstrates that a CNN can achieve precision and recall above 0.9 at detecting different types of weeds during turf establishment when the weeds are mature. The CNN is limited by the image resolution, and more than one model may be needed in practice to improve the overall performance of weed mapping.Jing ZhangJerome MaleskiDavid JespersenF. C. WaltzGlen RainsBrian SchwartzFrontiers Media S.A.articleBermudagrassartificial intelligenceFastaiResNetRGB imageryPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bermudagrass artificial intelligence Fastai ResNet RGB imagery Plant culture SB1-1110
spellingShingle	Bermudagrass artificial intelligence Fastai ResNet RGB imagery Plant culture SB1-1110 Jing Zhang Jerome Maleski David Jespersen F. C. Waltz Glen Rains Brian Schwartz Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
description	Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm–1.28 cm pixel–1 resolution. Recall ranges for the five classes were 0.78–0.93, 0.65–0.87, 0.82–0.93, 0.52–0.79, and 0.94–0.99. Additionally, this study demonstrates that a CNN can achieve precision and recall above 0.9 at detecting different types of weeds during turf establishment when the weeds are mature. The CNN is limited by the image resolution, and more than one model may be needed in practice to improve the overall performance of weed mapping.
format	article
author	Jing Zhang Jerome Maleski David Jespersen F. C. Waltz Glen Rains Brian Schwartz
author_facet	Jing Zhang Jerome Maleski David Jespersen F. C. Waltz Glen Rains Brian Schwartz
author_sort	Jing Zhang
title	Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
title_short	Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
title_full	Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
title_fullStr	Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
title_full_unstemmed	Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network
title_sort	unmanned aerial system-based weed mapping in sod production using a convolutional neural network
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/daf9bf860603469f842428ae02c0ee6f
work_keys_str_mv	AT jingzhang unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT jeromemaleski unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT davidjespersen unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT fcwaltz unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT glenrains unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork AT brianschwartz unmannedaerialsystembasedweedmappinginsodproductionusingaconvolutionalneuralnetwork
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Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network

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