Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics
Bibliographic record
Abstract
Remote sensing Vegetation Indices (VIs) are simple, effective, and widely used methods for quantitative and qualitative analysis of vegetation cover, vigor, and growth dynamics. This study developed and assessed a new vegetation index (VI) using Cyan, Orange, and Near Infrared (NIR) bands to assess Soybean growth dynamics. The study was conducted at Lakehead University Agriculture Research Station, Thunder Bay, Canada, over four reproductive stages of Soybean growth (R4–R7). Spectral profiles were created for each stage, and the correlation between each spectral band at different stages was tested. There was no linear correlation between different bands except the correlation between the Cyan and Orange bands at R5 and R6 stages. Existing VIs have also been explored using approximately similar band combinations. Based on this analysis, three VIs were proposed for this new camera, and their behavior at different stages was evaluated using Leaf Area Index (LAI). Cyan and Orange spectral values were relatively high in the first and last growing seasons, while NIR values increased dramatically in the mid-growing seasons and decreased in the last stage. VINIR,O,C index showed the best results for mid-growing seasons (correlation with LAI = 0.39 for R5 and R6). VIC,O index showed a high level of details visually (leaves and background) for R4 and R7 than the other indices and correlated highly with LAI (0.48 and −0.5, respectively). Overall, the study provided new VIs that can be used to effectively analyze Soybean growth dynamics, with different VIs showing reliability over different growing stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".