In‐Season Nitrogen Status Assessment and Yield Estimation Using Hyperspectral Vegetation Indices in a Potato Crop
Bibliographic record
Abstract
The rate and timing of N applications are important issues in precision agriculture because of the within‐field spatial and temporal variability of soil N availability. In‐season assessment of potato (Solanum tuberosum L.) crop N status (CNS) is required to better match N fertilizer supply to crop N demand and improve N use efficiency. The objective of this study was to investigate the ability of hyperspectral vegetation indices (HVIs) to assess the CNS and tuber yield of irrigated ‘Russet Burbank’ potato at different growth stages. A 2‐yr field experiment was conducted near Quebec City, QC, Canada, on plots receiving five different N rates ranging from 0 to 280 kg N ha−1, with 40% applied at planting and 60% at hilling. Entire plant samples were collected biweekly for determination of the N nutrition index (NNI) as the N status reference method. In‐field hyperspectral reflectance derived from a handheld spectroradiometer and using two fields of view (FOV; 7.5° and 25°) was obtained on several dates during both growing seasons. The sensitivity of the five HVIs most correlated to the NNI was evaluated by analyses of variance and least significant differences. It was found that HVIs computed from reflectance in the red‐edge spectral region and using a wider FOV were the most appropriate indices to detect potato crop N stress. Among these indices, the CI1red‐edge (red‐edge chlorophyll index 1) was the most sensitive to potato N content and could explain 76% of the variability in total tuber yield at 55 d after planting (DAP).
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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 | 0.000 | 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 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".