Weed recognition in corn fields using back-propagation neural network models
Bibliographic record
Abstract
in corn fields using back-propagation neural network models. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 44: 7.15-7.22. The objective of this study was to develop back-propagation artificial neural network (ANN) models to distinguish young corn plants from weeds. Digital images were taken in the field under various natural lighting conditions. The images were cropped and resized to smaller sub-images containing either corn plants or weeds. The green objects in the images were extracted with the greenness method, thus counting the pixels with a green intensity larger than red and blue intensities and replacing other background pixels with the intensity of zero. The extracted colour images were then converted to intensity images to save computational efforts during the ANN model development. The number of images available for training was quadrupled by rotating counter-clockwise each image by 90, 180, and 270 degrees. Several hundred images of corn plants and
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".