Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network

Abstract Goosegrass is a problematic weed species in Florida vegetable plasticulture production. To reduce costs associated with goosegrass control, a post-emergence precision applicator is under development for use atop the planting beds. To facilitate in situ goosegrass detection and spraying, tin...

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Autores principales:	Shaun M. Sharpe, Arnold W. Schumann, Nathan S. Boyd
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/370341e2e29243c18402c8f70cf92a24
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spelling	oai:doaj.org-article:370341e2e29243c18402c8f70cf92a242021-12-02T17:52:24ZGoosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network10.1038/s41598-020-66505-92045-2322https://doaj.org/article/370341e2e29243c18402c8f70cf92a242020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-66505-9https://doaj.org/toc/2045-2322Abstract Goosegrass is a problematic weed species in Florida vegetable plasticulture production. To reduce costs associated with goosegrass control, a post-emergence precision applicator is under development for use atop the planting beds. To facilitate in situ goosegrass detection and spraying, tiny- You Only Look Once 3 (YOLOv3-tiny) was evaluated as a potential detector. Two annotation techniques were evaluated: (1) annotation of the entire plant (EP) and (2) annotation of partial sections of the leaf blade (LB). For goosegrass detection in strawberry, the F-score was 0.75 and 0.85 for the EP and LB derived networks, respectively. For goosegrass detection in tomato, the F-score was 0.56 and 0.65 for the EP and LB derived networks, respectively. The LB derived networks increased recall at the cost of precision, compared to the EP derived networks. The LB annotation method demonstrated superior results within the context of production and precision spraying, ensuring more targets were sprayed with some over-spraying on false targets. The developed network provides online, real-time, and in situ detection capability for weed management field applications such as precision spraying and autonomous scouts.Shaun M. SharpeArnold W. SchumannNathan S. BoydNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shaun M. Sharpe Arnold W. Schumann Nathan S. Boyd Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
description	Abstract Goosegrass is a problematic weed species in Florida vegetable plasticulture production. To reduce costs associated with goosegrass control, a post-emergence precision applicator is under development for use atop the planting beds. To facilitate in situ goosegrass detection and spraying, tiny- You Only Look Once 3 (YOLOv3-tiny) was evaluated as a potential detector. Two annotation techniques were evaluated: (1) annotation of the entire plant (EP) and (2) annotation of partial sections of the leaf blade (LB). For goosegrass detection in strawberry, the F-score was 0.75 and 0.85 for the EP and LB derived networks, respectively. For goosegrass detection in tomato, the F-score was 0.56 and 0.65 for the EP and LB derived networks, respectively. The LB derived networks increased recall at the cost of precision, compared to the EP derived networks. The LB annotation method demonstrated superior results within the context of production and precision spraying, ensuring more targets were sprayed with some over-spraying on false targets. The developed network provides online, real-time, and in situ detection capability for weed management field applications such as precision spraying and autonomous scouts.
format	article
author	Shaun M. Sharpe Arnold W. Schumann Nathan S. Boyd
author_facet	Shaun M. Sharpe Arnold W. Schumann Nathan S. Boyd
author_sort	Shaun M. Sharpe
title	Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
title_short	Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
title_full	Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
title_fullStr	Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
title_full_unstemmed	Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network
title_sort	goosegrass detection in strawberry and tomato using a convolutional neural network
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/370341e2e29243c18402c8f70cf92a24
work_keys_str_mv	AT shaunmsharpe goosegrassdetectioninstrawberryandtomatousingaconvolutionalneuralnetwork AT arnoldwschumann goosegrassdetectioninstrawberryandtomatousingaconvolutionalneuralnetwork AT nathansboyd goosegrassdetectioninstrawberryandtomatousingaconvolutionalneuralnetwork
_version_	1718379218777145344

Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network

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