ContexNestedU-Net: Efficient Context-Aware Semantic Segmentation Architecture for Precision Agriculture Applications Based on Multispectral Remote Sensing Imagery
Bibliographic record
Abstract
Precision agriculture relies on semantic segmentation models to optimize crop yield and minimize environmental impact.ContexNestedU-Net is proposed to improve the capture of contextual information for efficient utilization of multispectral remote sensing images in precision agriculture applications.For this purpose, it includes a novel redesign of the convolutional blocks in the Nested U-Net model.Through the application of depthwise separable convolution in the convolution blocks, the ContexNestedU-Net efficiently preserves unique spectral information.Subsequently, dilated convolution is applied to capture rich contextual information.Three image sets are utilized in the experiments, one from the WorldView-3 satellite and the others from aerial vehicles.Extensive experiments demonstrate that the ContexNestedU-Net outperforms other U-Net-based models for various precision agriculture tasks.When using NDVI images, the proposed architecture improves the Jaccard index by 13% for tree objects, 0.9% for crop objects, and 4.5% for wheat yellow-rust objects compared to Nested U-Net.In addition, the ContexNestedU-Net model reduces the number of trainable parameters from 36.63 to 19 compared to Nested U-Net, and the computational complexity (GLOPs) decreases from 849.3 to 302.4.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".