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Detection of grassy weeds in bermudagrass with deep convolutional neural networks

Published online by Cambridge University Press:  08 June 2020

Jialin Yu Open the ORCID record for Jialin Yu [Opens in a new window] ,
Arnold W. Schumann ,
Shaun M. Sharpe ,
Xuehan Li  and
Nathan S. Boyd
Jialin Yu
Affiliation:
Professor, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu, China
Arnold W. Schumann
Affiliation:
Professor, Citrus Research and Education Center, University of Florida, Lake Alfred, Florida, USA
Shaun M. Sharpe
Affiliation:
Research Scientist, Saskatoon Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada/Government of Canada, Saskatoon, Saskatchewan, Canada
Xuehan Li
Affiliation:
Student, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu, China
Nathan S. Boyd*
Affiliation:
Associate Professor, Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, USA
*
Author for correspondence: Nathan S. Boyd, Gulf Coast Research and Education Center, University of Florida, 14625 County Road 672, Wimauma, FL33578. Email: [email protected]
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Abstract

Spot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed [Murdannia nudiflora (L.) Brenan], dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass [Urochloa distachya (L.) T.Q. Nguyen] in bermudagrass [Cynodon dactylon (L.) Pers.]. VGGNet generally outperformed AlexNet and GoogLeNet in detecting selected grassy weeds. For detection of P. dilatatum, VGGNet achieved high F1 scores (≥0.97) and recall values (≥0.99). A single VGGNet model exhibited high F1 scores (≥0.93) and recall values (1.00) that reliably detected Digitaria spp., M. nudiflora, P. dilatatum, and U. distachya. Low weed density reduced the recall values of AlexNet at detecting all weed species and GoogLeNet at detecting Digitaria spp. In comparison, VGGNet achieved excellent performances (overall accuracy = 1.00) at detecting all weed species in both high and low weed-density scenarios. These results demonstrate the feasibility of using DCNN for detection of grass or grass-like weeds in turfgrass systems.

Keywords

Artificial intelligencecomputer visionmachine visionprecision herbicide applicationweed recognition
Type
Research Article
Information
Weed Science , Volume 68 , Issue 5 , September 2020 , pp. 545 - 552
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Weed Science Society of America

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Footnotes

Associate Editor: Ramon G. Leon, North Carolina State University

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