Quantifying consistency of crop establishment using a lightweight U-Net deep learning architecture and image processing techniques
Bibliographic record
Abstract
Consistency of crop establishment is a measure of uniformity of crop attributes, such as plant stand count, crop emergence rate, and plant spacing across the field. Quantifying consistency during the early crop growth stage is important for establishment decisions to use targeted nutrients and to facilitate timely replanting in inconsistent crop regions. Crop consistency can be analysed using two key parameters: plant stand count and spacing statistics since they provide insight into plant density and its emergence percentage. However, manual assessment of them is time-consuming, prone to errors, and labour-intensive in large fields. An alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings. We use the YOLOv5-based object detection model for plant counting, which attains a mean average precision of 0.956 to detect Canola plants. A Lightweight U-Net model is proposed to segment rows, followed by Guo–Hall thinning and Probabilistic Hough Transform to determine inter-row and inter-plant spacing. Our proposed row segmentation model achieves a mean Intersection over Union (mIoU) of 0.8444 with class-wise IoU of 0.9925 and 0.6963 for background and crop using fewer parameters. The new architecture uses only 14M parameters and achieves performance comparable to the state-of-the-art U-Net (32.5M) and SegNet (29M).
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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 | 0.001 |
| 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) | 0.000 | 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".