The DOI matches its Crossref record.
Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions
Abstract
Color slide images of weeds among various soils and residues were digitized and analyzed for red, green, and blue (RGB) color content. Red, green, and blue chromatic coordinates (rgb) of plants were very different from those of background soils and residue. To distinguish living plant material from a nonplant background, several indices of chromatic coordinates were studied, tested, and were successful in identifying weeds. The indices included r-g, g-b, (g-b)/ |r-g|, and 2g-r-b. A modified hue was also used to distinguish weeds from non-plant surfaces. The modified hue, 2g-r-b index, and the green chromatic coordinate distinguished weeds from a nonplant background (0.05 level of significance) better than other indices. However, the modified hue was the most computationally intense. These indices worked well for both nonshaded and shaded sunlit conditions. These indices could be used for sensor design for detecting weeds for spot spraying control.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A survey of image processing techniques for plant extraction and segmentation in the field
Hamuda, E., Glavin, M., & Jones, E. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.04.024 · Cited by 587
Many Little Hammers: Ecological Management of Crop-Weed Interactions
Liebman, M., & Gallandt, E. R. (1997) · Ecology in Agriculture · doi:10.1016/b978-012378260-1/50010-5 · Cited by 328
Dang, F., Chen, D., Lu, Y., & Li, Z. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107655 · Cited by 313
Hamuda, E., Mc Ginley, B., Glavin, M., & Jones, E. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.11.021 · Cited by 267
(2006) · OpenAgrar · Cited by 25
Metadata
- Authors
- Woebbecke, D. M., Meyer, G. E., Bargen, K. V., & Mortensen, D. A.
- Venue
- Transactions of the ASAE
- Published
- 1995
- Volume
- 38
- Issue
- 1
- Pages
- 259-269
- ISSN
- 0001-2351 (ISSN-L)
- 2151-0059
- OpenAlex
- W2163450852
- Cited by
- 1,537
- References
- 0
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. https://app.cochat.ai/papers/10.13031/2013.27838
- The paper
- Woebbecke, D. M., Meyer, G. E., Bargen, K. V., & Mortensen, D. A. (1995). Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. Transactions of the ASAE. https://doi.org/10.13031/2013.27838
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.