Abstract
Corn and weed classification using video data is required to run in real-time. Video data processing is necessary to consider its speed and correct classification. Problems arise when the classification speed does not meet the video's frame per second (fps) specifications. This research proposes a combination of parallel processing and tracking to increase the speed of video data processing. The speed of video processing is also supported by the machine learning method. The combination of visual features through feature selection is also applied in this research. The results of the non-parallel experiment produced an average of 25.45 fps, while the proposed method produced 43.99 fps or an increase of 72.82%. This speed increase is due to parallel processing through MPI4Py at the pre-processing and color segmentation stages, which optimizes multi-core CPU resources. In addition, the feature extraction and classification stages also use skip frames so that not all frames are processed. The corn and weed classification results experienced a slight decrease. This decrease in classification results is not as significant as the resulting processing speed. Through these results, the proposed method enables real-time processing, making it useful for video data processing.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc. 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 2
Claim · model-written summary
The proposed parallel and tracking method raised corn and weed video processing speed to 43.99 fps, an increase of 72.82% over the non-parallel experiment at 25.45 fps.
- Taxon
- Corn
Zea mays subsp. mayse0 - Treatment
- parallel processinge2
- Outcome
- speede2
- Direction
- increasee1
- Value
- 43.99 fpse1
- Qualifier
- 25.45 fpse1
- Comparator
- non-parallel experimente1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Corn and weed classification using video data is required to run in real-time.”
e1“The results of the non-parallel experiment produced an average of 25.45 fps, while the proposed method produced 43.99 fps or an increase of 72.82%”
e2“This speed increase is due to parallel processing through MPI4Py at the pre-processing and color segmentation stages”
Claim · model-written summary
Corn and weed classification results decreased slightly under the proposed method.
- Taxon
- CornZea mays subsp. mayse0
- Outcome
- corn and weed classification resultse3
- Direction
- decreasee3
- Value
- slighte3
- Qualifier
- not as significant as the resulting processing speede4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Corn and weed classification using video data is required to run in real-time.”
e3“The corn and weed classification results experienced a slight decrease”
e4“This decrease in classification results is not as significant as the resulting processing speed”