End to End Segmentation of Canola Field Images Using Dilated U-Net
Bibliographic record
Abstract
Semantic segmentation is used in many fields like agriculture, medical imaging, and autonomous driving. The paper proposes an end to end solution for efficient weeds and crop segmentation in field environment application. The crop/weeds segmented output is utilized to generate a decision map for variable rate fertilizer and herbicide application. Currently available models are memory expensive and do not have real time performance unless enough computational power is accessible in field. We use Maximum Likelihood Classification (MLC) and image processing techniques to label field images in three classes; background, crop, and weeds. This data is processed through our modified U-Net, which improves the semantic accuracy with reduced memory cost. We train our model with DICE loss and compare the results with state of the art. We achieve 89.12% mean Intersection Over Union (mIOU) with 86.11%, 82.99%, and 98.23% individual IOU for crop, weeds, and background, respectively. Our proposed model uses only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15M$ </tex-math></inline-formula> parameters which are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$57M$ </tex-math></inline-formula> less than the state-of-the-art models with a compromise of 1% mIOU score.
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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) | 0.001 | 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 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".