Abstract
This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.
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 4
Claim · model-written summary
The IPSO algorithm reduced segmentation error rates to 0.1% from 7.1% for traditional particle swarm optimization.
- Taxon
- corn
Zea mays subsp. mayse1 - Organ / stage
- seedling growth stagee1
- Interactor
- weedse1weed
- Outcome
- segmentation error ratese0
- Direction
- reducede0
- Value
- 0.1%e0
- Comparator
- traditional particle swarm optimization algorithme0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm”
e1“successfully segmented weeds within corn crop at seedling growth stage”
Claim · model-written summary
The IPSO algorithm processed frames at 0.026 s per frame.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- processing speede2
- Value
- 0.026 s/framee2
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e2“IPSO processing speed was 0.026 s/frame”
e1“successfully segmented weeds within corn crop at seedling growth stage”
Claim · model-written summary
The weed detection to chemical actuation response time of the integrated system was 1.562 s.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- chemical actuation response timee3
- Value
- 1.562 se3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e3“The weed detection to chemical actuation response time of integrated system was 1.562 s”
e1“successfully segmented weeds within corn crop at seedling growth stage”
Claim · model-written summary
The VRHS system met the real-time data processing and actuation requirements for weed management.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- real-time data processing and actuation requirementse4
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e4“VRHS system met the real-time data processing and actuation requirements”
e1“successfully segmented weeds within corn crop at seedling growth stage”