DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage
Bibliographic record
Abstract
Farmers around the world face the challenge of growing more food for the increasing world population. On top of that, external threats such as pests (weeds and insects) pose a threat to crop production and it is necessary to take early steps to reduce the risk. This paper presents semantic segmentation of canola field images collected under natural conditions. The dataset contains four unbalanced classes; background, crop, weeds, and damages in the crop. The damages to the crop leaves are small round shaped and share the same texture and colour as whitish stones from the background. We propose, DeepVeg, a deep learning segmentation model that focuses on the smallest (damage) class without affecting other classes to solve the class imbalance issue. Early stage canola field image dataset is utilized for training and testing the proposed model. Evaluation results show that the proposed method outperforms the benchmark deep learning models and effectively addresses the weed and damaged canola plants segmentation problem. The DeepVeg model demonstrates a superior mean intersection over union score greater than 0.76 and$accuracy$above 0.97 for four class segmentation. The model also shows robustness in detecting unlabelled, newly grown weeds and canola and is also able to distinguish the similar rounded structured canola plant and weed with small amounts of data for model training, which is suitable for early stage damage and weed segmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".