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Seed rain potential in late-season weed escapes can be estimated using remote sensing

Published online by Cambridge University Press:  21 June 2021

Matthew Kutugata
Affiliation:
Graduate Student, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
Chengsong Hu
Affiliation:
Graduate Student, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA Graduate Student, Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA
Bishwa Sapkota
Affiliation:
Graduate Student, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
Muthukumar Bagavathiannan*
Affiliation:
Associate Professor, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
*
Author for correspondence: Muthukumar Bagavathiannan, Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Boulevard, College Station, TX 77845-2474. Email: muthu@tamu.edu

Abstract

The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled late-season weed escapes. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using unmanned aerial vehicle–based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes before crop harvest: common waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately before ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 to 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assisting with management decision making.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Weed Science Society of America

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Footnotes

Associate Editor: Prashant Jha, Iowa State University

*

These authors contributed equally to this work.

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