Weed Detection in Images of Carrot Fields Based on Improved YOLO v4
Why this work is in the frame
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Bibliographic record
Abstract
The accurate weed detection is the premise for precision prevention and control of weeds in fields. Machine vision offers an effective means to detect weeds accurately. For precision detection of various weeds in carrot fields, this paper improves You Only Look Once v4 (YOLO v4) into a lightweight weed detection model called YOLO v4-weeds for the weeds among carrot seedlings. Specifically, the backbone network of the original YOLOv4 was replaced with MobileNetV3-Small. Combined with depth-wise separable convolution and inverted residual structure, a lightweight attention mechanism was introduced to reduce the memory required to process images, making the detection model more efficient. The research results provide a reference for the weed detection, robot weeding, and selective spraying.
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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) | 0.001 | 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 it