Using remote sensing to detect weed infestations in Glycine max

The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populati...

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Bibliographic Details
Published in:Weed science 2000-05, Vol.48 (3), p.393-398
Main Authors: Medlin, Case R., Shaw, David R., Gerard, Patrick D., LaMastus, Falba E.
Format: Article
Language:English
Subjects:
Agrology
Crops
digital images
Discriminant analysis
Glycine max
Herbicides
Infestation
Ipomoea lacunosa
planting
Plants
Precision farming
Reflectance
Remote sensing
seedlings
Senna obtusifolia
site-specific agriculture
soil
Solanum carolinense
soybeans
summer
variable-rate application
vegetation
Weed control
WEED MANAGEMENT
Weeds
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Summary:The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populations were sampled using a regular grid coordinate system on a 10- by 10-m grid. Two days later, multispectral digital images of the fields were recorded. Generally, infestations of Senna obtusifolia,Ipomoea lacunosa, and Solanum carolinense could be detected with remote sensing with at least 75% accuracy. Threshold populations of 10 or more S. obtusifolia or I. lacunosa plants m−2 were generally classified with at least 85% accuracy. Discriminant analysis functions formed for detecting weed populations in one field were at least 73% accurate in identifying S. obtusifolia and I. lacunosa infestations in independently collected data from another field. Due to highly variable soil conditions and their effects on the reflectance properties of the surrounding soil and vegetation, accurate classification of weed-free areas was generally much lower. Current remote sensing technology has potential for in-season weed detection; however, further advancements of the technology are needed to insure its use in future prescription weed management systems. Nomenclature: Ipomoea lacunosa L. IPOLA, pitted morningglory; Senna obtusifolia (L.) Irwin et Barnaby CASOB, sicklepod; Solanum carolinense L. SOLCA, horsenettle; Glycine max (L.) Merr., soybean.
ISSN:0043-1745
1550-2759
DOI:10.1614/0043-1745%282000%29048%5B0393%3AURSTDW%5D2.0.CO%3B2