Exploring the potential of two vegetative indices, normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), to estimate balsam fir nutrition using uncrewed aerial vehicles and multispectral sensing
Bibliographic record
Abstract
Balsam fir trees are a specialty agricultural species that are largely used as Christmas trees. Soil or tissue sampling is seldom performed to assess balsam fir nutrition needs, which can contribute to over- or under-fertilization and subsequent environmental challenges. Remote sensing with vegetative indices has had strong relationships with nutrition in other plants. This research aimed to determine whether there is a relationship between normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2) and nutrition in balsam fir. A random sample of 45 trees was selected in autumn of 2021 and then another 70 trees selected in spring of 2022. Soil and needle tissue samples were analyzed for N, P, K, Ca, Mg, S, Fe, Zn, Mn, B, and Cu. An uncrewed aerial vehicle equipped with a multispectral camera was flown over the orchard to determine the NDVI/EVI2 for each tree. Data were analyzed for correlations through all sampled trees. Separate correlation analyses were performed for mature trees (greater than 5 years old) and young trees (less than 5 years old). Soil nutrients N, P, and K had a significant relationship with NDVI in autumn sampling. Only tissue N was correlated consistently with NDVI in both autumn and spring, with R2 of 37% and 33%, respectively. Relationships were much stronger when only mature trees were considered, increasing to 60% and 51%, respectively. Tissue N was correlated consistently with EVI in both autumn and spring, with R2 of 58% and 40%, respectively, but had no improvement in mature trees. Ultimately both NDVI and EVI2 had predictive power for tissue N that can be useful to producers to help optimize fertilizer input. However, further work is needed to refine these relationships.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".