Crop and Weed Leaf Area Index Mapping Using Multi-Source Remote and Proximal Sensing
Bibliographic record
Abstract
Site specific management rationalizes farm inputs and mitigate environmental impacts. Traditionally, low resolution satellite imagery and soil maps are employed for site specific decisions in large scale farms. However, these approaches are not good at sub-field level due to low spatial resolution. To overcome this problem, either manual scouting is employed or extensive high resolution data collection platforms are used. In both cases, the cost outweighs the expected returns. Consequently, variable rate applications are not preferred in large fields. Leaf Area Index (LAI) is a useful measure to monitor crop growth and health for site specific management. In this paper we propose an accurate and scalable process where multispectral remote sensing and proximal sensing data is used to estimate LAI. Crop LAI (CLAI) and Weed LAI (WLAI) are estimated from limited high resolution ground image samples using semantic segmentation. These limited LAIs are extended to the whole field using remote sensing and proximal sensing data. We find that LAIs are spatially related with Soil, Water and Topography (SWAT) maps and are field specific. With increasing weed population in the fields, correlation of WLAI with the SWAT zone increases. However, CLAI remains comparatively consistent across SWAT zones due to variable rate seeding and fertilizer application based on soil variance. Our results demonstrate that LAIs can be predicted accurately from limited high resolution ground imagery, satellite imagery, SWAT, and soil properties maps.
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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) | 0.000 | 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".