Soybean canopy nitrogen monitoring and prediction using ground based multispectral remote sensors
Bibliographic record
Abstract
Remote sensing techniques applied in crop monitoring and management can help to reduce the input of nitrogen without reducing crop yield and accurately predict nitrogen demand [1]. The objective of this study is to use the ground based multispectral images to predict canopy nitrogen level for soybeans in southwestern Ontario. A light weight multispectral camera were used to collect multispectral measurements for four soybean fields from July to September in 2015. An evaluation of existing nitrogen indices were carried on in this study for soybean canopy nitrogen to select the best fit index for the study area. The results show that the modified RENDVI780-730has the best correction between soybean nitrogen level and the spectral based index, the R2is 0.70. This index is sensitive to vegetation structures Leaf Area Index (LAI) which is a confounding factor for the remote estimation of nitrogen. This index will lead an inaccuracy nitrogen prediction for soybeans. Therefore, multi-linear regression (MLR) analysis method using five band information was carried on and established a canopy nitrogen model for soybeans in this study. The R2of the model is 0.745 and the RMSE is 0.51.
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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) | 0.000 | 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".