Mapping Soil Organic Matter Under Field Conditions
Bibliographic record
Abstract
Soil Organic Matter (SOM) is a key component for sustainable agriculture planning and soil management. Nutrient analysis, spectroscopy and digital soil imaging are commonly used to estimate SOM in a controlled lab setting. These methods are accurate, but the controlled lab setting is not scalable. For scalability, high-resolution satellite imagery is widely employed. However, special conditions of the Canadian Prairies, like harsh weather and crop residue cover, pose significant challenges in getting the spectral signatures of bare soil. To overcome these challenges, this paper presents a novel methodology that explores the prospects of using high-resolution ground images acquired under Uncontrolled Field Conditions (UFC) for SOM estimation. The developed methodology first extracts bare soil from images using deep learning methods. As the image samples are acquired under UFC, variable ambient illumination influences soil colour. To counter this, in the second step, we propose unsupervised colour constancy to mitigate the effects of variable ambient lighting conditions. In the third step, colour space and texture features are extracted to estimate SOM. We compare our proposed method with the state-of-the-art (SOTA) SOM estimation methods. We also performed an ablation study to compare the results of the SOTA with and without the addition of the colour constancy block. With the developed methodology, our bare soil segmentation model achieves a mean intersection over union value of 0.8134. Similarly, with the colour constancy methods applied on bare soil segmented images, our proposed method improves the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> score by more than 30% with respect to the SOTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.010 | 0.005 |
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; both teacher heads agree on what is shown here.
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".