Abstract
This study presents the evaluation of tools for weed analysis and management to support agroecological practices in organic farming, emphasizing agriculture digitalization, and remote sensing. The main aim was to provide techniques for monitoring and predicting weed spread using multispectral satellite and drone data, without the use of chemical inputs. Key findings indicate that VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density. However, RE and NIR channels of drone detected significant variations and proved effectiveness for weediness monitoring. The NIR channel is sensitive to agrotechnical factors such as cultivation type, making it valuable for field monitoring. Correlation and regression analyses revealed that B2, B3, B8 channels of Sentinel-2, and RE and NIR drone channels are the most reliable for predicting weed levels. Conversely, Sentinel-1 showed limited predictive utility. Random effect models confirmed that Sentinel-2 and drone channels can accurately account for site characteristics and timing of weed proliferation. Taken together, these tools provide effective organic weed monitoring systems, enabling rapid identification of problem areas and adjustments in agronomic practices.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 9
Claim · model-written summary
VV and VH channels of Sentinel-1 did not differ with tillage, herbicide use or sowing density.
- Treatment
- tillage, herbicide use, or sowing densitye0
- Outcome
- VV and VH channels of Sentinel-1e0
- Direction
- not differente0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density”
Claim · model-written summary
B2, B3 and B4 channels of Sentinel-2 did not differ with tillage, herbicide use or sowing density.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density”
Claim · model-written summary
B8 channel of Sentinel-2 did not differ with tillage, herbicide use or sowing density.
- Treatment
- tillage, herbicide use, or sowing densitye0
- Outcome
- B8 channels of Sentinel-2e0
- Direction
- not differente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density”
Claim · model-written summary
RE and NIR drone channels detected significant variations in weediness.
- Outcome
- RE and NIR channels of dronee1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e1“However, RE and NIR channels of drone detected significant variations and proved effectiveness for weediness monitoring”
Claim · model-written summary
RE and NIR drone channels proved effective for weediness monitoring.
- Outcome
- effectiveness for weediness monitoringe1
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e1“However, RE and NIR channels of drone detected significant variations and proved effectiveness for weediness monitoring”
Claim · model-written summary
The NIR channel is sensitive to agrotechnical factors such as cultivation type.
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e2“The NIR channel is sensitive to agrotechnical factors such as cultivation type, making it valuable for field monitoring”
Claim · model-written summary
B2, B3 and B8 channels of Sentinel-2 and RE and NIR drone channels are the most reliable for predicting weed levels.
- Outcome
- most reliable for predicting weed levelse3
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e3“Correlation and regression analyses revealed that B2, B3, B8 channels of Sentinel-2, and RE and NIR drone channels are the most reliable for predicting weed levels”
Claim · model-written summary
Sentinel-1 showed limited predictive utility for weed levels.
- Outcome
- limited predictive utilitye4
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e4“Conversely, Sentinel-1 showed limited predictive utility”
Claim · model-written summary
Sentinel-2 and drone channels can accurately account for site characteristics and timing of weed proliferation.
- Outcome
- accurately account for site characteristics and timing of weed proliferatione5
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e5“Random effect models confirmed that Sentinel-2 and drone channels can accurately account for site characteristics and timing of weed proliferation”